System and method for selecting a financial instrument to trade based on a match of a risk profile in computer platforms designed for improved electronic execution of electronic transactions

ABSTRACT

Computer-implemented methods and computer systems for an electronic transaction platform that enables the buying and/or selling of securities by users. The methods and systems relate to the identification and selection of one or more financial instruments to trade during an electronic communication session for trading a financial instrument between an initiating user and one or more invitee users with the assistance of a dealer user or other intermediate entity. The selected financial instrument is identified based on a degree of match between the preferred yield risk profile of the initiating user and the risk profiles associated with the financial instruments that are available to be traded.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 17/175,417 filed Feb. 12, 2021, which is acontinuation-in-part of U.S. Non-Provisional patent application Ser. No.16/879,327 filed May 20, 2020 which issued as U.S. Pat. No. 10,922,773on Feb. 16, 2021, which is a continuation of U.S. Non-Provisional patentapplication Ser. No. 16/805,401 filed Feb. 28, 2020, which claimspriority to U.S. Provisional patent application Ser. No. 62/812,602filed on Mar. 1, 2019, each of which is incorporated herein by referencein its entirety.

TECHNICAL FIELD

The present disclosure is related to computer-implemented methods andcomputer systems for an electronic transaction platform and, moreparticularly, to an electronic transaction platform that enables thebuying and/or selling of securities.

BACKGROUND

In the field of electronic trading, a user can buy and/or sellsecurities through use of a computer system implementing an electronictransaction platform. In such a platform, typically the user can enteran offer to buy or sell (a “bid” or “offer” respectively, which will bereferred to herein as an “offer” for the sake of simplicity) a security,which is then shared with other user(s) for completing a transaction.Additionally, a user can review other users' offers for purchases orsales, which can be acted upon to complete a transaction. Theimplementation of such an electronic transaction platform relies uponthe transfer of electronic communications over networks and betweenclient devices. These communications are defined by protocols enablingthe exchange of packets of information. In network communicationsamongst groups, these packets of information may be viewable by allparties, e.g., all information is available to all parties and allparties are typically allowed the same level of participation. Forsituations in which communications between limited groups ofparticipants, networking and network communications technologies provideinsufficient control of information access.

SUMMARY

In various implementations, the present disclosure is directed to acomputer-implemented method of hosting an electronic communicationsession for trading a financial instrument between an initiating userand one or more invitee users. The method can include receiving, at acomputing device having one or more processors, a request to begin theelectronic communication session from an initiating user. The requestcan identify the financial instrument to be traded and various terms atwhich the initiating user is willing to trade the financial instrument.The method can include identifying, at the computing device, one or moredealer users capable of acting as an intermediate entity for trading thefinancial instrument based on the request. The method can furtherinclude providing, from the computing device and to the initiating user,a list of the one or more identified dealer users and receiving, at thecomputing device and from the initiating user, a selection of aparticular dealer user based on the list of the one or more identifieddealer users. The method can also include receiving, at the computingdevice and from the particular dealer user, a list of the one or moreinvitee users to participate in the electronic communication session.The method can include transmitting, from the computing device and toeach of the one or more invitee users, an invitation to participate inthe electronic communication session. Further, the method can includeestablishing, at the computing device, the electronic communicationsession. The electronic communication session can permit each of the oneor more invitee users to submit an offer to trade the financialinstrument with the initiating user. Each offer can comprise anidentification of its associated invitee user and trade parametersincluding a price and a quantity of the financial instrument. The methodcan also include selectively sharing, from the computing device,information regarding offers received during the electroniccommunication session with each of the initiating user, the dealer user,and the one or more invitee users. The initiating user can receive thetrade parameters for each received offer. The dealer user can receivethe identification of the trade parameters for each received offer andits associated invitee user. Each particular invitee user can receiveeither: (i) the trade parameters for each received offer when theparticular invitee user has submitted its own offer, or (ii) noinformation regarding the received offers when the particular inviteeuser has not yet submitted its own offer.

In some aspects, the identifying the one or more dealer users capable ofacting as the intermediate entity for trading the financial instrumentcan comprise: determining a liquidity score for the financial instrumentbased on historical trading data for one or more other financialinstruments related to the financial instrument; identifying historicaldealers associated with the historical trading data.

In some aspects, determining the liquidity score for the financialinstrument can be based on a machine learning model.

In some aspects, the machine learning model determines the liquidityscore based on a number of historical transactions and a volume ofhistorical transactions for the financial instrument to be traded.

In some aspects, the machine learning model determines the liquidityscore based on a count ratio of the number of historical transactionsfor the financial instrument to be traded to a maximum number ofhistorical transactions for any financial instrument observed.

In some aspects, the machine learning model determines the liquidityscore based on a volume ratio of the volume of historical transactionsfor the financial instrument to be traded to a maximum volume ofhistorical transactions for any financial instrument observed.

In some aspects, the machine learning model determines the liquidityscore based on a combination of the count ratio and the volume ratio.

In some aspects, the machine learning model determines the liquidityscore based on a volume ratio of the volume of historical transactionsfor the financial instrument to be traded to a maximum volume ofhistorical transactions for any financial instrument observed.

In some aspects, the identifying the one or more dealer users capable ofacting as the intermediate entity for trading the financial instrumentcomprises: determining a dealer specific liquidity score for thefinancial instrument based on historical trading data for one or moreother financial instruments related to the financial instrument;identifying historical dealers associated with the historical tradingdata.

In some aspects, determining the dealer specific liquidity score for thefinancial instrument is based on a machine learning model.

In some aspects, the machine learning model determines the dealerspecific liquidity score based on a number of historical transactionswith a particular dealer and a volume of historical transactions for thefinancial instrument to be traded with the particular dealer.

In some aspects, the machine learning model determines the dealerspecific liquidity score based on a dealer specific count ratio of thenumber of historical transactions for the financial instrument to betraded with the particular dealer to a maximum number of historicaltransactions for any financial instrument observed with the particulardealer.

In some aspects, the machine learning model determines the dealerspecific liquidity score based on a dealer specific volume ratio of thevolume of historical transactions for the financial instrument to betraded with the particular dealer to a maximum volume of historicaltransactions for any financial instrument observed with the particulardealer.

In some aspects, the machine learning model determines the dealerspecific liquidity score based on a combination of the dealer specificcount ratio and the dealer specific volume ratio.

In some aspects, the machine learning model determines the dealerspecific liquidity score based on a volume ratio of the volume ofhistorical transactions for the financial instrument to be traded withthe particular dealer to a maximum volume of historical transactions forany financial instrument observed with the particular dealer.

In some aspects, the machine learning model determines the liquidityscore based on a pricing deviation score corresponding to a differencebetween a traded pricing and a desired pricing for the financialinstrument to be traded, the traded pricing comprising a previous priceat which the financial instrument was traded and the desired pricingcomprising a desired price at which the initiating user is willing totrade the financial instrument.

In some aspects, the previous price at which the financial instrumentwas traded comprises an end of day price for the financial instrument.

In some aspects, the pricing deviation score is based on the equation:

${\Delta L{S\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{\iota}}*0.5}$

where ΔLS(F(P_(i))) is the pricing deviation score for a financialinstrument i; REFP_(i) is the reference pricing of a recently completedtrade for the financial instrument i; STD_(i) is an average standarddeviation of the reference pricing and trade price for the financialinstrument i; and IP_(i) is the desired price for the financialinstrument i.

In some aspects, the identifying the one or more dealer users capable ofacting as the intermediate entity for trading the financial instrumentcomprises: determining a customer specific liquidity score for thefinancial instrument based on historical trading data for one or moreother financial instruments related to the financial instrument;identifying historical dealers associated with the historical tradingdata.

In some aspects, determining the customer specific liquidity score forthe financial instrument is based on a machine learning model.

In some aspects, the machine learning model determines a customer valuethat is indicative of a probability that a specific customer willexecute a trade for the security at the trade parameters, wherein thecustomer specific liquidity score for the financial instrument is basedon the customer value.

In some aspects, the machine learning model determines the customerspecific liquidity score based on LSi=(LSmarket+10*(CACi/2), 10), whereLSi is the customer specific liquidity score for customer i; LSmarket isa liquidity score for the financial instrument irrespective of customer;and CACi is the customer value.

In some aspects, the identifying the one or more dealer users capable ofacting as the intermediate entity for trading the financial instrumentcomprises: determining a cloud score for the financial instrument basedon trading intentions of the one or more invitee users and the variousterms, wherein the cloud score is representative of a likelihood thatthe financial instrument will be traded at the various terms;identifying at least one high likelihood invitee user of the one or moreinvitee users based on the cloud score, wherein the at least one highlikelihood invitee user has expressed trading intentions compatible withsuccessfully trading the financial instrument with the initiating userat the various terms; and identifying the one or more dealer usershaving a previous trading relationship with the at least one highlikelihood invitee user.

In some aspects, determining the cloud score for the financialinstrument is based on a machine learning model.

In some aspects, the machine learning model determines the cloud scorebased on a degree of matching between the trading intentions of the oneor more invitee users and the various terms.

In some aspects, the degree of matching is based on a price matchingscore and a quantity matching score.

In some aspects, the price matching score corresponds to a price degreeof matching between a trading price of the trading intentions of the oneor more invitee users and a desired price of the various terms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the degree of matching is expressed as a number between0 and 1.

In some aspects, the method can further comprise: determining, at thecomputing device, a cloud score for the financial instrument based ontrading intentions of a group of potential invitee users and the variousterms, wherein the cloud score is representative of a likelihood thatthe financial instrument will be traded at the various terms; ranking,at the computing device, the group of potential invitee users into aranked list based on the cloud score to identify one or more highlikelihood potential invitee users that have expressed tradingintentions compatible with successfully trading the financial instrumentwith the initiating user at the various terms; and providing, from thecomputing device and to the particular dealer user, the ranked list. Theparticular dealer user can select the list of the one or more inviteeusers to participate in the electronic communication session from theranked list.

In some aspects, the ranked list is limited to a predetermined number ofpotential invitee users.

In some aspects, determining the cloud score for the financialinstrument is based on a machine learning model.

In some aspects, the machine learning model determines the cloud scorebased on a degree of matching between the trading intentions of the oneor more invitee users and the various terms.

In some aspects, the degree of matching is based on a price matchingscore and a quantity matching score.

In some aspects, the price matching score corresponds to a price degreeof matching between a trading price of the trading intentions of the oneor more invitee users and a desired price of the various terms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the degree of matching is expressed as a number between0 and 1.

In various implementations, the present disclosure is directed to acomputer-implemented method of determining a likelihood of successfullytrading a particular financial instrument between an initiating user andone or more invitee users and at various terms during an electroniccommunication session. The method can include receiving, at a computingdevice having one or more processors, historical trading data associatedwith previously executed trade transactions for a plurality of financialinstruments. Each of the plurality of financial instruments can haveassociated characteristics, the associated characteristics including atleast an expected return, and the trade transactions identify at least aprice and quantity traded. The method can further include storing, atthe computing device, the historical trading data, and identifying, atthe computing device, one or more of the plurality of financialinstruments having associated characteristics similar to the particularfinancial instrument. The method can also include determining, at thecomputing device and using a first machine learning model, a liquidityscore for the particular financial instrument based on the historicaltrading data for the one or more of the plurality of financialinstruments having associated characteristics similar to the particularfinancial instrument. The liquidity score can be representative of alikelihood of successfully trading the particular financial instrumentat the various terms.

In some aspects, the first machine learning model determines theliquidity score based on a number of historical transactions and avolume of historical transactions for the financial instrument to betraded.

In some aspects, the first machine learning model determines theliquidity score based on a count ratio of the number of historicaltransactions for the financial instrument to be traded to a maximumnumber of historical transactions for any financial instrument observed.

In some aspects, the machine learning model determines the liquidityscore based on a volume ratio of the volume of historical transactionsfor the financial instrument to be traded to a maximum volume ofhistorical transactions for any financial instrument observed.

In some aspects, the machine learning model determines the liquidityscore based on a combination of the count ratio and the volume ratio.

In some aspects, the machine learning model determines the liquidityscore based on a volume ratio of the volume of historical transactionsfor the financial instrument to be traded to a maximum volume ofhistorical transactions for any financial instrument observed.

In some aspects, the liquidity score is based on a dealer specificliquidity score.

In some aspects, the first machine learning model determines the dealerspecific liquidity score based on a number of historical transactionswith a particular dealer and a volume of historical transactions for thefinancial instrument to be traded with the particular dealer.

In some aspects, the first machine learning model determines the dealerspecific liquidity score based on a dealer specific count ratio of thenumber of historical transactions for the financial instrument to betraded with the particular dealer to a maximum number of historicaltransactions for any financial instrument observed with the particulardealer.

In some aspects, the first machine learning model determines the dealerspecific liquidity score based on a dealer specific volume ratio of thevolume of historical transactions for the financial instrument to betraded with the particular dealer to a maximum volume of historicaltransactions for any financial instrument observed with the particulardealer.

In some aspects, the first machine learning model determines the dealerspecific liquidity score based on a combination of the dealer specificcount ratio and the dealer specific volume ratio.

In some aspects, the first machine learning model determines the dealerspecific liquidity score based on a volume ratio of the volume ofhistorical transactions for the financial instrument to be traded withthe particular dealer to a maximum volume of historical transactions forany financial instrument observed with the particular dealer.

In some aspects, the first machine learning model determines theliquidity score based on a pricing deviation score corresponding to adifference between a traded pricing and a desired pricing for thefinancial instrument to be traded, the traded pricing comprising aprevious price at which the financial instrument was traded and thedesired pricing comprising a desired price at which the initiating useris willing to trade the financial instrument.

In some aspects, the previous price at which the financial instrumentwas traded comprises an end of day price for the financial instrument.

In some aspects, the pricing deviation score is based on the equation:

${\Delta L{S\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

where ΔLS(F(Pi)) is the pricing deviation score for a financialinstrument i; REFPi is the reference pricing of a recently completedtrade for the financial instrument i; STDi is an average standarddeviation of the reference pricing and trade price for the financialinstrument i; and IPi is the desired price for the financial instrumenti.

In some aspects, the liquidity score is based on a customer specificliquidity score.

In some aspects, the first machine learning model determines a customervalue that is indicative of a probability that a specific customer willexecute a trade for the security at the trade parameters, wherein thecustomer specific liquidity score for the financial instrument is basedon the customer value.

In some aspects, the machine learning model determines the customerspecific liquidity score based on LSi=(LSmarket+10*(CACi/2), 10), whereLSi is the customer specific liquidity score for customer i; LSmarket isa liquidity score for the financial instrument irrespective of customer;and CACi is the customer value.

In some aspects, the method can further comprise: receiving, at thecomputing device, trading intentions from the one or more invitee users,each trading intention representing a price and a quantity at which itsassociated invitee user would trade an associated financial instrument,each associated financial instrument having associated characteristics,the associated characteristics including at least an expected return;storing, at the computing device, the trading intentions; identifying,at the computing device, one or more associated financial instrumentssimilar to the particular financial instrument; and determining, at thecomputing device and using a second machine learning model, a cloudscore for the particular financial instrument based on the tradingintentions for the one or more associated financial instrument similarto the particular financial instrument, the cloud score beingrepresentative of a likelihood of successfully trading the particularfinancial instrument at the various terms.

In some aspects, the second machine learning model determines the cloudscore based on a degree of matching between the trading intentions ofthe one or more invitee users and the various terms.

In some aspects, the degree of matching is based on a price matchingscore and a quantity matching score.

In some aspects, the price matching score corresponds to a price degreeof matching between a trading price of the trading intentions of the oneor more invitee users and a desired price of the various terms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the degree of matching is expressed as a number between0 and 1.

In some aspects, the method can further comprise outputting, from thecomputing device and to an initiating user computing device associatedwith the initiating user, the liquidity score and the cloud score in agraphical user interface.

In some aspects, outputting the liquidity score and the cloud score inthe graphical user interface comprises outputting a single scorerepresenting a combination of the liquidity score and the cloud score.

In various implementations, the present disclosure is directed to acomputer-implemented method of determining a likelihood of successfullytrading a particular financial instrument between an initiating user andone or more invitee users and at various terms during an electroniccommunication session. The method can comprise receiving, at thecomputing device, trading intentions from the one or more invitee users.Each trading intention can represent a price and a quantity at which itsassociated invitee user would trade an associated financial instrument.Each associated financial instrument can have associatedcharacteristics. The associated characteristics can include at least anexpected return. The method can also include storing, at the computingdevice, the trading intentions and identifying, at the computing device,one or more associated financial instruments similar to the particularfinancial instrument. The method can further include determining, at thecomputing device and using a machine learning model, a cloud score forthe particular financial instrument based on the trading intentions forthe one or more associated financial instrument similar to theparticular financial instrument, the cloud score being representative ofa likelihood of successfully trading the particular financial instrumentat the various terms.

In some aspects, the machine learning model determines the cloud scorebased on a degree of matching between the trading intentions of the oneor more invitee users and the various terms.

In some aspects, the degree of matching is based on a price matchingscore and a quantity matching score.

In some aspects, the price matching score corresponds to a price degreeof matching between a trading price of the trading intentions of the oneor more invitee users and a desired price of the various terms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the degree of matching is expressed as a number between0 and 1.

In some aspects, the method can further comprise outputting, from thecomputing device and to an initiating user computing device associatedwith the initiating user, the cloud score in a graphical user interface.

In various implementations, the present disclosure is directed to acomputer-implemented method comprising hosting, by a computing devicehaving one or more processors, an electronic communication session fortrading a financial instrument between an initiating user and one ormore invitee users. The hosting can include receiving, by the computingdevice, a request to begin the electronic communication session from aninitiating user computing device associated with an initiating user, therequest identifying the financial instrument to be traded and variousterms at which the initiating user is willing to trade the financialinstrument. The hosting can also comprise matching, by the computingdevice, the request with capabilities of a plurality of dealer users toidentify one or more dealer users capable of acting as an intermediateentity for trading the financial instrument. A list of the one or moreidentified dealers can be provided, by the computing device, to theinitiating user computing device associated with the initiating user.The hosting can further comprise receiving, by the computing device andfrom the initiating user computing device associated with the initiatinguser, a selection of a particular dealer user based on the list of theone or more identified dealer users. In response to receiving theselection of the particular dealer user, the computing device canestablish the electronic communication session. The establishing of theelectronic communication session can include receiving, by the computingdevice and from a dealer user computing device associated with theparticular dealer user, a list of the one or more invitee users toparticipate in the electronic communication session. In response toreceiving the list of the one or more invitee users, the computingdevice can transmit, to each invitee user computing device associatedwith each of the one or more invitee users, an invitation to participatein the electronic communication session. The hosting can also includecoordinating, by the computing device, communications during theelectronic communication session, wherein the electronic communicationsession permits each of the one or more invitee users to submit an offerto trade the financial instrument with the initiating user, each offercomprising an identification of its associated invitee user and tradeparameters including a price and a quantity of the financial instrument.The coordination of communications during the electronic communicationsession can comprise selectively sharing, by the computing device,information regarding offers received during the electroniccommunication session with each of the initiating user, the dealer user,and the one or more invitee users, wherein the initiating user receivesthe trade parameters for each received offer, the dealer user receivesthe identification of the trade parameters for each received offer andits associated invitee user, and each particular invitee user receives:(i) the trade parameters for each received offer when the particularinvitee user has submitted its own offer, and (ii) no informationregarding the received offers when the particular invitee user has notyet submitted its own offer.

In some aspects, the identifying the one or more dealer users capable ofacting as the intermediate entity for trading the financial instrumentcan comprise: determining a liquidity score for the financial instrumentbased on historical trading data for one or more other financialinstruments related to the financial instrument; identifying historicaldealers associated with the historical trading data.

In some aspects, determining the liquidity score for the financialinstrument can be based on a liquidity score machine learning model.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a number of historical transactions and avolume of historical transactions for the financial instrument to betraded.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a count ratio of the number of historicaltransactions for the financial instrument to be traded to a maximumnumber of historical transactions for any financial instrument observed.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a volume ratio of the volume of historicaltransactions for the financial instrument to be traded to a maximumvolume of historical transactions for any financial instrument observed.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a combination of the count ratio and thevolume ratio.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a volume ratio of the volume of historicaltransactions for the financial instrument to be traded to a maximumvolume of historical transactions for any financial instrument observed.

In some aspects, the identifying the one or more dealer users capable ofacting as the intermediate entity for trading the financial instrumentcomprises: determining a dealer specific liquidity score for thefinancial instrument based on historical trading data for one or moreother financial instruments related to the financial instrument;identifying historical dealers associated with the historical tradingdata.

In some aspects, determining the dealer specific liquidity score for thefinancial instrument is based on a liquidity score machine learningmodel.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a number of historicaltransactions with a particular dealer and a volume of historicaltransactions for the financial instrument to be traded with theparticular dealer.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a dealer specific countratio of the number of historical transactions for the financialinstrument to be traded with the particular dealer to a maximum numberof historical transactions for any financial instrument observed withthe particular dealer.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a dealer specific volumeratio of the volume of historical transactions for the financialinstrument to be traded with the particular dealer to a maximum volumeof historical transactions for any financial instrument observed withthe particular dealer.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a combination of the dealerspecific count ratio and the dealer specific volume ratio.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a volume ratio of thevolume of historical transactions for the financial instrument to betraded with the particular dealer to a maximum volume of historicaltransactions for any financial instrument observed with the particulardealer.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a pricing deviation score corresponding toa difference between a traded pricing and a desired pricing for thefinancial instrument to be traded, the traded pricing comprising aprevious price at which the financial instrument was traded and thedesired pricing comprising a desired price at which the initiating useris willing to trade the financial instrument.

In some aspects, the previous price at which the financial instrumentwas traded comprises an end of day price for the financial instrument.

In some aspects, the pricing deviation score is based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

-   -   where ΔLS(F(P_(i))) is the pricing deviation score for a        financial instrument i; REFP_(i) is the reference pricing of a        recently completed trade for the financial instrument i; STD_(i)        is an average standard deviation of the reference pricing and        trade price for the financial instrument i; and IP_(i) is the        desired price for the financial instrument i.

In some aspects, the identifying the one or more dealer users capable ofacting as the intermediate entity for trading the financial instrumentcomprises: determining a customer specific liquidity score for thefinancial instrument based on historical trading data for one or moreother financial instruments related to the financial instrument;identifying historical dealers associated with the historical tradingdata.

In some aspects, determining the customer specific liquidity score forthe financial instrument is based on a liquidity score machine learningmodel.

In some aspects, the liquidity score machine learning model determines acustomer value that is indicative of a probability that a specificcustomer will execute a trade for the security at the trade parameters,wherein the customer specific liquidity score for the financialinstrument is based on the customer value.

In some aspects, the liquidity score machine learning model determinesthe customer specific liquidity score based onLSi=(LSmarket+10*(CACi/2), 10), where LSi is the customer specificliquidity score for customer i; LSmarket is a liquidity score for thefinancial instrument irrespective of customer; and CACi is the customervalue.

In some aspects, the identifying the one or more dealer users capable ofacting as the intermediate entity for trading the financial instrumentcomprises: determining a cloud score for the financial instrument basedon trading intentions of the one or more invitee users and the variousterms, wherein the cloud score is representative of a likelihood thatthe financial instrument will be traded at the various terms;identifying at least one high likelihood invitee user of the one or moreinvitee users based on the cloud score, wherein the at least one highlikelihood invitee user has expressed trading intentions compatible withsuccessfully trading the financial instrument with the initiating userat the various terms; and identifying the one or more dealer usershaving a previous trading relationship with the at least one highlikelihood invitee user.

In some aspects, determining the cloud score for the financialinstrument is based on a cloud score machine learning model.

In some aspects, the cloud score machine learning model determines thecloud score based on a degree of matching between the trading intentionsof the one or more invitee users and the various terms.

In some aspects, the degree of matching is based on a price matchingscore and a quantity matching score.

In some aspects, the price matching score corresponds to a price degreeof matching between a trading price of the trading intentions of the oneor more invitee users and a desired price of the various terms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the degree of matching is expressed as a number between0 and 1.

In some aspects, the method can further comprise: determining, at thecomputing device, a cloud score for the financial instrument based ontrading intentions of a group of potential invitee users and the variousterms, wherein the cloud score is representative of a likelihood thatthe financial instrument will be traded at the various terms; ranking,at the computing device, the group of potential invitee users into aranked list based on the cloud score to identify one or more highlikelihood potential invitee users that have expressed tradingintentions compatible with successfully trading the financial instrumentwith the initiating user at the various terms; and providing, from thecomputing device and to the particular dealer user, the ranked list. Theparticular dealer user can select the list of the one or more inviteeusers to participate in the electronic communication session from theranked list.

In some aspects, the ranked list is limited to a predetermined number ofpotential invitee users.

In some aspects, determining the cloud score for the financialinstrument is based on a cloud score machine learning model.

In some aspects, the cloud score machine learning model determines thecloud score based on a degree of matching between the trading intentionsof the one or more invitee users and the various terms.

In some aspects, the degree of matching is based on a price matchingscore and a quantity matching score.

In some aspects, the price matching score corresponds to a price degreeof matching between a trading price of the trading intentions of the oneor more invitee users and a desired price of the various terms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the degree of matching is expressed as a number between0 and 1.

In various implementations, the present disclosure is directed to acomputer-implemented method comprising determining, by a computingdevice having one or more processors, a likelihood of executing a tradeof a particular financial instrument between an initiating user and oneor more invitee users and at various terms during an electroniccommunication session. The determining can include receiving, by thecomputing device, historical trading data associated with previouslyexecuted trade transactions for a plurality of financial instruments,wherein each of the plurality of financial instruments has associatedinstrument characteristics, the associated instrument characteristicsincluding at least an expected return, and the trade transactionsidentify at least a price and quantity traded. The determining can alsoinclude performing, by the computing device, cluster analysis of theparticular financial instrument and the plurality of financialinstruments to identify one or more of the plurality of financialinstruments having associated instrument characteristics that matchinstrument characteristics of the particular financial instrument.Further, the determining can include determining, by the computingdevice and using a liquidity score machine learning model, a liquidityscore for the particular financial instrument based on the historicaltrading data for the one or more of the plurality of financialinstruments having associated instrument characteristics that match theinstrument characteristics of the particular financial instrument, theliquidity score being representative of a likelihood of executing atrade of the particular financial instrument at the various terms. Themethod can additionally include ranking, by the computing device, theparticular financial instrument and other financial instruments into aranked list based on the liquidity score and other liquidity scoresassociated with the other financial instruments and presenting, by thecomputing device, the ranked list in a graphical user interface of aninitiating user computing device associated with the initiating user.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a number of historical transactions and avolume of historical transactions for the particular financialinstrument to be traded.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a count ratio of the number of historicaltransactions for the particular financial instrument to be traded to amaximum number of historical transactions for any financial instrumentobserved.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a volume ratio of the volume of historicaltransactions for the particular financial instrument to be traded to amaximum volume of historical transactions for any financial instrumentobserved.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a combination of the count ratio and thevolume ratio.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a volume ratio of the volume of historicaltransactions for the particular financial instrument to be traded to amaximum volume of historical transactions for any financial instrumentobserved.

In some aspects, the liquidity score is based on a dealer specificliquidity score.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a number of historicaltransactions with a particular dealer and a volume of historicaltransactions for the particular financial instrument to be traded withthe particular dealer.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a dealer specific countratio of the number of historical transactions for the particularfinancial instrument to be traded with the particular dealer to amaximum number of historical transactions for any financial instrumentobserved with the particular dealer.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a dealer specific volumeratio of the volume of historical transactions for the particularfinancial instrument to be traded with the particular dealer to amaximum volume of historical transactions for any financial instrumentobserved with the particular dealer.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a combination of the dealerspecific count ratio and the dealer specific volume ratio.

In some aspects, the liquidity score machine learning model determinesthe dealer specific liquidity score based on a volume ratio of thevolume of historical transactions for the particular financialinstrument to be traded with the particular dealer to a maximum volumeof historical transactions for any financial instrument observed withthe particular dealer.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a pricing deviation score corresponding toa difference between a traded pricing and a desired pricing for theparticular financial instrument to be traded, the traded pricingcomprising a previous price at which the particular financial instrumentwas traded and the desired pricing comprising a desired price at whichthe initiating user is willing to trade the particular financialinstrument.

In some aspects, the previous price at which the particular financialinstrument was traded comprises an end of day price for the particularfinancial instrument.

In some aspects, the pricing deviation score is based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

where ΔLS(F(Pi)) is the pricing deviation score for a financialinstrument i; REFPi is the reference pricing of a recently completedtrade for the financial instrument i; STDi is an average standarddeviation of the reference pricing and trade price for the financialinstrument i; and IPi is the desired price for the financial instrumenti.

In some aspects, the liquidity score is based on a customer specificliquidity score.

In some aspects, the liquidity score machine learning model determines acustomer value that is indicative of a probability that a specificcustomer will execute a trade for the particular financial instrument atthe trade parameters, wherein the customer specific liquidity score forthe particular financial instrument is based on the customer value.

In some aspects, the liquidity score machine learning model determinesthe customer specific liquidity score based onLSi=(LSmarket+10*(CACi/2), 10), where LSi is the customer specificliquidity score for customer i; LSmarket is a liquidity score for theparticular financial instrument irrespective of customer; and CACi isthe customer value.

In some aspects, the method can further comprise: receiving, at thecomputing device, trading intentions from one or more invitee usercomputing devices associated with the one or more invitee users, eachtrading intention representing a price and a quantity at which itsassociated invitee user would trade an associated financial instrument,each associated financial instrument having associated instrumentcharacteristics, the associated instrument characteristics including atleast an expected return; storing, at the computing device, the tradingintentions; performing, by the computing device, cluster analysis of theparticular financial instrument and the associated financial instrumentsto identify one or more associated financial instruments havingassociated instrument characteristics that match instrumentcharacteristics of the particular financial instrument; determining, bythe computing device and using a cloud score machine learning model, acloud score for the particular financial instrument based on the tradingintentions for the one or more associated financial instruments havingassociated instrument characteristics that match the instrumentcharacteristics of the particular financial instrument, the cloud scorebeing representative of a likelihood of executing the trade of theparticular financial instrument at the various terms; and outputting, bythe computing device and to the initiating user computing deviceassociated with the initiating user, the liquidity score and the cloudscore in the graphical user interface.

In some aspects, the cloud score machine learning model determines thecloud score based on a degree of matching between the trading intentionsof the one or more invitee users and the various terms.

In some aspects, the degree of matching is based on a price matchingscore and a quantity matching score.

In some aspects, the price matching score corresponds to a price degreeof matching between a trading price of the trading intentions of the oneor more invitee users and a desired price of the various terms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the quantity matching score corresponds to a quantitydegree of matching between a trading quantity of the trading intentionsof the one or more invitee users and a desired quantity of the variousterms.

In some aspects, the degree of matching is expressed as a number between0 and 1.

In some aspects, outputting the liquidity score and the cloud score inthe graphical user interface comprises outputting a single scorerepresenting a combination of the liquidity score and the cloud score.

In various implementations, the present disclosure is directed to acomputer-implemented method comprising determining, by a computingdevice having one or more processors, a likelihood of executing a tradeof a particular financial instrument between an initiating user and oneor more invitee users and at various terms during an electroniccommunication session. The determining can include receiving, by thecomputing device, trading intentions from one or more invitee usercomputing devices associated with the one or more invitee users, eachtrading intention representing a price and a quantity at which itsassociated invitee user would trade an associated financial instrument,each associated financial instrument having associated instrumentcharacteristics, the associated instrument characteristics including atleast an expected return. The determining can also include performing,by the computing device, cluster analysis of the particular financialinstrument and the associated financial instruments to identify one ormore associated financial instruments having associated instrumentcharacteristics that match instrument characteristics of the particularfinancial instrument. Further, the determining can include determining,by the computing device and using a cloud score machine learning model,a cloud score for the particular financial instrument based on thetrading intentions for the one or more associated financial instrumentshaving associated instrument characteristics that match the instrumentcharacteristics of the particular financial instrument, the cloud scorebeing representative of a likelihood of executing the trade of theparticular financial instrument at the various terms. The method canadditionally comprise ranking, by the computing device, the particularfinancial instrument and other financial instruments into a ranked listbased on the cloud score and other cloud scores associated with theother financial instruments and presenting, by the computing device, theranked list in a graphical user interface of an initiating usercomputing device associated with the initiating user.

In some aspects, the cloud score machine learning model can determinethe cloud score based on a degree of matching between the tradingintentions of the one or more invitee users and the various terms.

In some aspects, the degree of matching can be based on a price matchingscore and a quantity matching score.

In some aspects, the price matching score can correspond to a pricedegree of matching between a trading price of the trading intentions ofthe one or more invitee users and a desired price of the various terms.

In some aspects, the quantity matching score can correspond to aquantity degree of matching between a trading quantity of the tradingintentions of the one or more invitee users and a desired quantity ofthe various terms.

In some aspects, the quantity matching score can correspond to aquantity degree of matching between a trading quantity of the tradingintentions of the one or more invitee users and a desired quantity ofthe various terms.

In some aspects, the degree of matching can be expressed as a numberbetween 0 and 1.

In some aspects, the determining can further include receiving, by thecomputing device, historical trading data associated with previouslyexecuted trade transactions for a plurality of historically tradedfinancial instruments, wherein each of the plurality of historicallytraded financial instruments has associated historically tradedinstrument characteristics, the associated historically tradedinstrument characteristics including at least an expected return, andthe trade transactions identify at least a price and quantity traded;performing, by the computing device, cluster analysis of the particularfinancial instrument and the plurality of historically traded financialinstruments to identify one or more of the plurality of historicallytraded financial instruments having associated historically tradedinstrument characteristics that match the instrument characteristics ofthe particular financial instrument; and determining, by the computingdevice and using a liquidity score machine learning model, a liquidityscore for the particular financial instrument based on the historicaltrading data for the one or more of the plurality of historically tradedfinancial instruments having associated historically traded instrumentcharacteristics that match the instrument characteristics of theparticular financial instrument, the liquidity score beingrepresentative of the likelihood of executing the trade of theparticular financial instrument at the various terms, wherein the rankedlist is further based on the liquidity score.

In some aspects, the liquidity score machine learning model candetermine the liquidity score based on a number of historicaltransactions and a volume of historical transactions for the particularfinancial instrument to be traded.

In some aspects, the liquidity score machine learning model candetermine the liquidity score based on a count ratio of the number ofhistorical transactions for the particular financial instrument to betraded to a maximum number of historical transactions for any financialinstrument observed.

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a volume ratio of the volume of historicaltransactions for the particular financial instrument to be traded to amaximum volume of historical transactions for any financial instrumentobserved

In some aspects, the liquidity score machine learning model determinesthe liquidity score based on a combination of the count ratio and thevolume ratio.

In some aspects, the liquidity score can be based on a dealer specificliquidity score.

In some aspects, the liquidity score machine learning model candetermine the dealer specific liquidity score based on a number ofhistorical transactions with a particular dealer and a volume ofhistorical transactions for the particular financial instrument to betraded with the particular dealer.

In some aspects, the liquidity score machine learning model candetermine the dealer specific liquidity score based on a dealer specificcount ratio of the number of historical transactions for the particularfinancial instrument to be traded with the particular dealer to amaximum number of historical transactions for any financial instrumentobserved with the particular dealer.

In some aspects, the liquidity score machine learning model candetermine the dealer specific liquidity score based on a dealer specificvolume ratio of the volume of historical transactions for the particularfinancial instrument to be traded with the particular dealer to amaximum volume of historical transactions for any financial instrumentobserved with the particular dealer

In some aspects, the liquidity score machine learning model candetermine the dealer specific liquidity score based on a combination ofthe dealer specific count ratio and the dealer specific volume ratio.

In some aspects, the liquidity score machine learning model candetermine the dealer specific liquidity score based on a volume ratio ofthe volume of historical transactions for the particular financialinstrument to be traded with the particular dealer to a maximum volumeof historical transactions for any financial instrument observed withthe particular dealer.

In various implementations, the present disclosure is directed to acomputing device for hosting an electronic communication session fortrading a financial instrument between an initiating user and one ormore invitee users. The computing device can comprise one or moreprocessors, and a non-transitory computer-readable storage medium havinga plurality of instructions stored thereon, which, when executed by theone or more processors, cause the one or more processors to performoperations. The operations can include any one or more of the operationsof the computer-implemented method of hosting an electroniccommunication session for trading a financial instrument between aninitiating user and one or more invitee users described above.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more fully understood by reference to thefollowing drawings which are for illustrative purposes only:

FIG. 1 is a diagram of an example computing environment includingexample user computing devices and an example server computing deviceaccording to some implementations of the present disclosure;

FIG. 2 is a functional block diagram of the example user computingdevices of FIG. 1;

FIG. 3A is an exemplary neural network model that can be used with thetechniques according to some implementations of the present disclosure;

FIG. 3B shows exemplary inputs into the exemplary inventive neuralnetwork of the present disclosure (e.g., as shown in FIG. 3A);

FIG. 4 shows exemplary testing of exemplary predictive models based ontraining and validation scores to determine when an optimal predictivemodel to be used by the exemplary inventive analytics module may existfor various analytics detailed herein;

FIGS. 5A-5C shows exemplary statistics utilized for training exemplarypredictive models;

FIG. 6 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure in a form of dashboard showing a state of theexemplary inventive liquidity cloud at a particular time;

FIG. 7 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure in a form of dashboard showing pre-trade intentionsof a particular user (e.g., initiating user) of the exemplary inventiveelectronic execution-mediating platform of the present disclosure in theexemplary inventive liquidity cloud at a particular time;

FIG. 8 is a screenshot of the exemplary inventive specialized GUI ofFIG. 7 with a pop-up inventive specialized GUI showing historical dataregarding a particular liquidity score;

FIG. 9 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure that a particular initiating user can use to set-upparameters of a desired electronic communication session (e.g., targetdealer(s), buy/sell indicator, corporate bond, amount, price, etc.);

FIG. 10 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure that a particular dealer can use to track request tohost electronic communication session(s);

FIG. 11 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure that a particular invitee can use to makesubmission(s) in to a particular electronic communication session;

FIGS. 12-16 are screenshots of various states of exemplary inventivespecialized stack GUIs of the present disclosure that are presented tothe initiating user (Seller), the electronic communication dealer, andan invitee (Bidder) during a particular electronic communicationsession;

FIG. 17 illustrates an exemplary environment in which some embodimentsof the exemplary inventive electronic execution-mediating platform ofthe present disclosure may operate; and.

FIGS. 18 and 19 illustrate schematics of exemplary implementations ofthe cloud computing/architectures that the exemplary inventiveelectronic execution-mediating platform of the present disclosure may beconfigured as in accordance with at least some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure, briefly summarized above anddiscussed in greater detail below, can be understood by reference to theillustrative embodiments of the present disclosure depicted in theappended drawings. It is to be noted, however, that the appendeddrawings illustrate only typical embodiments of this present disclosureand are therefore not to be considered limiting of its scope, for thepresent disclosure may admit to other equally effective embodiments.

As briefly mentioned above, conventional electronic transactionplatforms provide for various users to transmit electroniccommunications regarding the purchase/sale of securities. Suchelectronic transaction platforms typically operate in an “open” mannerwhere information regarding an offer is provided as quickly andseamlessly as possible and to as many users as possible in order toeffectuate a “free market” transaction. Examples of such an electronictransaction platform include any of the various stock selling/purchasingplatforms available to consumers. These conventional electronictransaction platforms are suitable for the trading of securities (suchas stocks) that are easily marketed and bought/sold, e.g., via a tradingfloor or other centralized location where trading takes place (auctionmarket).

In an over-the-counter (“OTC”) or similar market (such as the bondmarket), however, these conventional electronic transaction platformsmay be unsuitable for use due to various considerations. For exampleonly, at least partly due to relatively low number of usersparticipating in an OTC market, there can be a risk of one user's intentto buy/sell a specific security—if widely known by other users—affectingthe price at which the user is able to trade the security. Further,e.g., in the case of the bond market, the pricing of one specificsecurity may more directly affect the price of another security if thetwo securities share the same/similar characteristics. A seller of asecurity that is similar to another security may be able to obtain apricing advantage if the seller is aware of current offers for the othersecurity. Thus, the behaviour of users may be tailored to avoid“information leakage” in excess of what is required/prudent to be sharedin order to conduct a transaction. Theoretically, a user would prefer toonly share information regarding an offer with other user(s) that arelikely to accept the offer.

In order to address the above and other issues, the present disclosureis directed to improved computer-implemented methods and computersystems for an electronic transaction platform. Although not limited tosuch implementations, the disclosed techniques are particularly wellsuited for the trading of securities in an OTC or similar market inwhich the need for finding suitable trading partners is to be balancedagainst the widespread dissemination of information related to thepricing of the securities.

The present disclosure includes at least three features that differ fromconventional electronic transaction platforms. The first feature is atrading platform referred to herein as the RFX platform. The RFXplatform, among other aspects, provides users with a computer basedmarket for the buying and selling of securities that mitigates thedisadvantages of conventional electronic transaction platforms (e.g.,information leakage). The second feature is a scoring functionality thatprovides a relative ranking of securities by their propensity to tradefrom historical trading data (liquidity score) and/or based on expressedtrading intentions by other users (cloud score). The scoringfunctionality is based on a machine learning model that takes intoaccount various features/factors. The third feature is a partneridentification technique that permits a user to identify other usersthat have a relatively high likelihood of a successful trade for aspecific security. Each of these, and other features, will be describedmore fully below. Additionally, although these features may be describedseparately from one another, one advantage of the disclosed techniquesis the ability to combine some or all of these features in a singleelectronic transaction platform to provide a powerful research andtrading tool for users interested in trading securities.

The RFX platform provides a user with the ability to initiate apotential trading transaction session with one or more others usersthrough a dealer in a manner that limits information leakage. In aconventional transaction, such as in the bond market, a party wishing totrade a bond approaches a dealer with a potential transaction offer. Thedealer then approaches other parties who may be interested in executingthe trade on the opposite side. In order to protect against informationleakage in such a conventional transaction, the original user may onlycontact a specific dealer that he or she is confident can execute atrade. Similarly, the selected dealer may have connections to a limitedpool of other parties who may be interested in executing the trade onthe opposite side. In such a scenario, the original party may miss theopportunity to trade the security with a different dealer that dealswith a different pool of other users, e.g., due to the lack ofinformation that the original party has regarding the actual demand forthe security.

The RFX platform facilitates the matching of users with other userswilling to execute a specific trade of a security. The RFX platformpermits a party wishing to trade a security to initiate an RFXcommunication session. In one aspect, the initiating party may select adealer for the RFX communication session and provides the relevanttrading details. The dealer then has the opportunity to accept theselection, pass on the selection, or propose a modification of thetrading details. Upon acceptance of the selection or modification (bythe initiating user), the dealer can schedule the RFX communicationsession and invite a plurality of other users (invitees) in whom thedealer has confidence would be interested in the security. The selectionof the invitees by the dealer will create a closed universe ofpotentially interested parties that are able to view the securitytrading details and participate (e.g., execute a trade of the security)in the RFX communication session. In some aspects, the dealer can beassisted in this invitation process by the partner identificationtechniques described below.

When the RFX communication session begins, each of the invitees shallhave access to information regarding the initial pricing and quantity ofthe security being traded. The initiating user can choose the initialpricing and quantity at which the initiating user will trade thesecurity, which will constitute a firm commitment to trade the securityat those prices/quantity. In some implementations, an invitee may gainaccess to additional information after making an offer to trade thesecurity. For example only, upon submission of an offer an invitee maybe able to view all current offers submitted by other invitees. In someaspects, each invitee can be anonymized, such as by being given apseudonym, such that other invitees and the initiating user are unawareof the actual parties. Each offer by an invitee is a firm commitment totrade the security at the offered rate for the given quantity. The RFXcommunication session can be “live” for a given period of time, e.g., asdetermined by the dealer or otherwise. At the end of the RFXcommunication session, and assuming that acceptable offers users havebeen made, the dealer can complete the transaction between theinitiating user and invitee(s).

The scoring functionality mentioned above can permit a user to gaugeinterest in a specific trade before initiating an RFX communicationsession. The scoring functionality can be based on one or both ofhistorical trading data (previously executed trades) and tradingintentions of other users known by the platform (current demand).Historical trading data is published and can be ingested into theelectronic transaction platform. Additionally, users can input theirtrading intentions into the electronic transaction platform. The tradingintentions can include an identification of the security to be traded, adirection of trade intention (buy or sell), a trade quantity, and atrade price. A machine learning model can be trained to calculate thelikelihood of a successful trade transaction based on the historicaltrading data and/or trading intentions and generate one or more scoresto provide to a user with respect to a specific trading intention. Thescore(s) can assist users in pricing a security before providinginformation to other users, thereby avoiding information leakage.

As briefly mentioned above, the partner identification technique permitsa user or dealer to identify the most likely users in their customerbase to complete a successful trade for a specific security. In someaspects, a machine learning model is utilized to identify user(s) thathave expressed interest in a specific security and/or other, similarsecurities, and/or have traded the same/similar security in the past. Acertain number of users with the highest rankings for a given set ofinputs (top-10, top-25, etc.) can be invited to participate in an RFXcommunication session as invitees if the user decides to initiate such asession. In this manner, a user can be more likely to be paired with aninvitee that is willing to conduct a trade for a given offering, whilealso limiting the information leakage to parties with whom the user isunlikely to successfully trade.

It is understood that at least one aspect/functionality of variousimplementations described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the terms“real-time processing,” “real-time computation,” and “real-timeexecution” all pertain to the performance of a computation during theactual time that the related physical process (e.g., a user interactingwith an application on a mobile device) occurs, such that results of thecomputation can be used in guiding the physical process. For example, insome embodiments, an event occurs in real-time if a time differencebetween a first time when an exemplary order is received and a secondtime when a dealer's makes the exemplary offer for counterbidding is nomore than 10 minutes. For example, in some embodiments, an event occursin real-time if a time difference between a first time when an exemplaryorder is received and a second time when a dealer's makes the exemplaryoffer for counterbidding is no more than 1 second. For example, in someembodiments, an event occurs in real-time if a time difference between afirst time when an exemplary order is received and a second time when adealer's makes the exemplary offer for counterbidding is between lessthan 1 second and 10 minutes.

As used herein, the term “dynamically” means that events and/or actionscan be triggered and/or occur without any human intervention. In someembodiments, events and/or actions in accordance with the presentdisclosure can be in real-time and/or based on a predeterminedperiodicity of at least one of: nanosecond, several nanoseconds,millisecond, several milliseconds, second, several seconds, minute,several minutes, hourly, several hours, daily, several days, weekly,monthly, etc. As used herein, the terms “dynamically” and automaticallyare used interchangeably and have the same meaning.

As used herein, the term “runtime” corresponds to any behaviour that isdynamically determined during an execution of a software application orat least a portion of software application.

In some embodiments, the inventive specially programmed computingsystems with associated devices are configured to operate in thedistributed network environment, communicating over a suitable datacommunication network (e.g., the Internet, etc.) and utilizing at leastone suitable data communication protocol (e.g., IPX/SPX, X.25, AX.25,AppleTalk™, TCP/IP (e.g., HTTP), etc.). Of note, the embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages. In this regard, those ofordinary skill in the art are well versed in the type of computerhardware that may be used, the type of computer programming techniquesthat may be used (e.g., object-oriented programming), and the type ofcomputer programming languages that may be used (e.g., C++, Objective-C,Swift, Java, JavaScript). The aforementioned examples are, of course,illustrative and not restrictive.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

In another form, a non-transitory article, such as a non-transitorycomputer readable medium, may be used with any of the examples mentionedabove or other examples except that it does not include a transitorysignal per se. It does include those elements other than a signal per sethat may hold data temporarily in a “transitory” fashion such as RAM andso forth.

As used herein, the terms “computer engine” and “engine” identify atleast one software component and/or a combination of at least onesoftware component and at least one hardware component which aredesigned/programmed/configured to manage/control other software and/orhardware components (such as the libraries, software development kits(SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Examples of software may include software components, programs,applications, computer programs, application programs, system programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof.Determining whether an embodiment is implemented using hardware elementsand/or software elements may vary in accordance with any number offactors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor.

As used herein, the term “user” shall have a meaning of at least oneuser.

As used herein, the term “backstop” identifies a bid or offer (anelectronic firm trading submission), depending on the direction of anexemplary inventive electronic communication session detailed that maybe provided by the electronic communication dealer at the start of theelectronic communication session, prior to invitees being able to placea bid/offer. For example, the price and/or size provided by theelectronic communication dealer is firm and will result in a trade atthe conclusion of the electronic communication, unless the electroniccommunication dealer's bid/offer is not allocated in the electroniccommunication due to more competitive bids/offers.

As used herein, the term “buyside” identifies market participants insecurities which generally refer to investment firms that purchasesecurities, such as asset managers. For example, buyside marketparticipants may include, but are not limited to, mutual funds, pensionfunds, family offices, hedge funds, and private equity funds.

As used herein, the term “committed level” identifies a firm price levelset by the initiating user that the initiating user will trade (i.e.,buy or sell depending on the side) the specific financial instrument(e.g., corporate bond).

As used herein, the term “dealer” identifies a market participant thatacts as an intermediary in the securities markets by buying and/orselling securities on its own account before selling the securities to acustomer and provides the sellers, buyers or both, access to thesettlement system for the trades that are executed. In some embodiments,these are characterized as “riskless principal” trades.

As used herein, the term “intermediate entity” identifies a singledealer associated with the particular electronic communication sessionwho accepted an invitation from the initiating user and/or fromexemplary inventive electronic execution-mediating platform of thepresent disclosure to act as the dealer to host an electroniccommunication session. In some embodiments, the dealer may runelectronic communication sessions where the dealer is both theinitiating user and electronic communication dealer

As used herein, the term “initiating user” identifies a user thatdesires to initiate an electronic inventive electronic communicationsession and who will either purchase or sell securities at theconclusion of the electronic communication session if the initiatinguser's pre-determined parameters are met. For example, an exemplaryinitiating user may be a member of the membership network of users thatthe exemplary inventive electronic execution-mediating platform of thepresent disclosure is configured to operate.

As used herein, the term “invitee” identifies a user/party that isinvited to act as a invitee to an electronic inventive electroniccommunication session (opportunity to bid or offer, depending on thedirection of the electronic communication session, on the security thatis the subject of the electronic communication session) based at leastin part on: i) selection by the electronic communication dealer; ii)expressed pre-trade intention; and/or iii) determination by theexemplary inventive electronic execution-mediating platform of thepresent disclosure base at least in part on data of the liquiditydatabase.

As used herein, the term “pre-trade intention” identifies an expressionof interest (electronically set preference and/or electronic submission)by a user in buying and/or selling a particular financialinstrument/security (e.g., corporate bond) at a given price for a givensize.

As used herein, the terms “liquidity monitor” and “liquidity dashboard”interchangeably identify one or more specialized inventive graphicaluser interfaces (GUIs) that display a liquidity score for each financialinstrument/security (e.g., each corporate bond).

As used herein, the term “electronic communication dealer's liquidityscore” identifies a calculated score assigned to each electroniccommunication dealer for each financial instrument (e.g., each corporatebond) and/or a calculated score based on data including, but not limitedto, historical trading data of similar corporate bonds by eachrespective dealer, as determined by the exemplary inventive electronicexecution-mediating platform of the present disclosure.

As used herein, the terms “financial instrument's liquidity score” or“corporate bond's liquidity score” identifies a calculated scoreassigned to each financial instrument (e.g., each corporate bond) basedon data including, but not limited to, historical trading data of theparticular corporate bond and/or similar corporate bond(s), asdetermined by the exemplary inventive electronic execution-mediatingplatform of the present disclosure.

As used herein, the terms “liquidity cloud” identifies a combination ofspecifically one or more programmed software/hardware modules incommunication with one or more electronic distribute and/or centralizeddatabases that may be programmed with suitable analytical logic detailedherein, and continuously updated with users' (e.g., members/users of theexemplary inventive electronic execution-mediating platform of thepresent disclosure) list of financial instruments (e.g., corporatebonds) that they have a specific interest in bidding/offering on (i.e.,pre-trade intentions) in the event that another market participantinitiates an electronic communication session. For example,members/users of the exemplary inventive electronic execution-mediatingplatform of the present disclosure are able to upload existing corporatebond positions that they want the exemplary inventive electronicexecution-mediating platform of the present disclosure to provideanalytics on to the liquidity cloud directly or from their OrderManagement System. For example, in some embodiments, once the corporatebond positions are added to the liquidity cloud, the liquidity cloud'sanalytics module may generate and present to a particular user aliquidity score, based on a number of factors, including but not limitedto other market participants' pre-trade intentions uploaded to theliquidity cloud, that estimate the likelihood that there is contrainterest in the individual corporate bond. For example, the user may bealso provided with a liquidity score for each corporate bond positionbased on data including, but not limited to, TRACE data and user'shistorical data to determine how much liquidity there is in the marketfor that corporate bond. For example, in addition to uploading existingpositions which they are interested in selling, clients can uploadcorporate bond positions that they would be interested in purchasing ifthey became available. In some embodiments, the availability of variousliquidity scores allows the exemplary inventive electronicexecution-mediating platform of the present disclosure to increase thespeed of trade execution due to users' ability to make their decisionsquicker (e.g., in real-time).

As used herein, the term “stack GUI(s)” identifies one or morespecialized inventive GUIs that is/are configured to display the priceand size that invitees have submitted for the financial instrument(e.g., a corporate bond) that is the subject of a particular inventiveelectronic communication session.

As used herein, the term “TRACE” identifies the Trade Reporting andCompliance Engine hosted by FINRA which provides a historicalperspective of the trade in over-the-counter markets for U.S. corporatebonds, agency debentures, asset-backed and mortgage backed securitymarkets.

As used herein, the term “post-trade” identifies a state at theconclusion of a particular electronic communication session when theexemplary inventive electronic execution-mediating platform of thepresent disclosure is configured to generate electronic notifications toall invitees that received an allocation in the electronic communicationsession.

The various systems and methods of the present disclosure will bedescribed in reference to a computing environment 100 shown in FIG. 1.It should be appreciated, however, that the illustrated computingenvironment 100 is merely an example and the techniques of the presentdisclosure are equally applicable to other computing environments thatinclude more or less elements.

Referring now to FIG. 1, a diagram of an example computing environment100 is illustrated. The computing environment 100 can include a firstuser computing device 104 that can communicate with a second usercomputing device 108 via a network 112. While mobile phoneconfigurations of the user computing devices 104, 108 are illustrated,it will be appreciated that the first and second user computing devices104, 108 can be any suitable computing devices configured forcommunication via the network 112 (desktop computers, laptop computers,tablet computers, etc.). The network 112 can be a cellular network (2G,3G, 4G long term evolution (LTE), etc.), a computing network (local areanetwork, the Internet, etc.), or some combination thereof. A servercomputing device 116 can also communicate via the network 112. Forexample only, the server computing device 116 could facilitate,implement, coordinate, manage, etc. an electronic communication sessionbetween a first user 120 and a second user 124 associated with the firstand second client computing devices 104, 108, respectively. In someaspects, the server computing device 116 can be associated with anintermediate entity (such as a dealer) that is not illustrated.

Referring now to FIG. 2, a functional block diagram of an examplecomputing device 200 is illustrated. The computing device 200 canrepresent the configurations of the first and second computing devices104, 108. It will be appreciated that the server computing device 116could also have the same or similar configuration as the computingdevice 200. The computing device 200 can include a communication device204 (e.g., a wireless transceiver) configured for communication via thenetwork 112. A processor 208 can be configured to control operation ofthe computing device 200. The term “processor” as used herein can referto both a single processor and two or more processors operating in aparallel or distributed architecture. A memory 212 can be any suitablestorage medium (flash, hard disk, etc.) configured to store informationat the computing device 200. In one implementation, the memory 212 canstore instructions executable by the processor 208 to cause thecomputing device 200 to perform at least a portion of the disclosedtechniques.

The computing device 200 can also include an input device 216 (such as akeyboard) and a display 220. The input device 216 can be any suitablehardware device (such as a keyboard) that permits a user to provideinput to the computing device 200. While not shown, it will beappreciated that the computing device 200 can include other suitablecomponents, such as a microphone, a speaker, physical buttons, a camera,and the like. As further described below, the example computingenvironment 100 can be configured to perform various techniques forhosting, supporting, participating, etc. in an electronic communicationsession.

In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured toallow initiating users to decide on which dealer to request to be theelectronic communication dealer based at least in part on whichdealer(s) each respective initiating user has an existing tradingrelationship and/or the dealers' liquidity scores. In some embodiments,the exemplary inventive electronic execution-mediating platform of thepresent disclosure is configured to require the initiating user toselect a number of parameters including, but not limited to, the firmprice at which the initiating user will buy or sell the selectedcorporate bond, the minimum size that needs to be filled for theinitiating user to agree to buy or sell the selected corporate bond, andthe criteria for allowing invitees to see invitee's submissions at orthe stack GUI itself (locked, unlocked, open). In some embodiments, theelectronic communication dealer and the respective initiating userelectronically can agree on a number of parameters including but notlimited to, transaction fee paid to the electronic communication dealer,the maximum number of invitees that the electronic communication dealercan invite, and the length of time of the electronic communicationsession. In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured toallow the electronic communication dealer to select an option to eitheri) fill the full quantity of corporate bonds at a pre-determined priceor ii) provide a backstop. In some embodiments, a “Buy Now” option maybe employed that allows invited participants, provided they accept thefull transaction amount, to immediately agree to the transaction.

For example, if the electronic communication dealer does not agree tofill the full quantity of initiating user's electronic communicationsession, the electronic communication dealer will invite its selectedinvitees to participate in the electronic communication. In someembodiments, the electronic communication dealer's list of invitees maybe informed by invitees' rankings generated by the exemplary inventiveelectronic execution-mediating platform of the present disclosure basedon the information in the liquidity cloud (e.g., in an associateddatabase) and/or the electronic communication dealer's own data. Forexample, additionally, unless the initiating user has requestedotherwise, the exemplary inventive electronic execution-mediatingplatform of the present disclosure would also provide selected inviteesthat are able to be invitees in the particular electronic communicationsession based at least in part on their pre-trade intentions in specificfinancial instruments (e.g., corporate bonds).

In some embodiments, an electronic execution-mediating cloud platformmay be configured to facilitate communication amongst parties withimproved control over access to information and permission toparticipate compared to technology known to the field of electroniccommunication and networking. In some applications, it is beneficial tocontrol which participants participate in a communication session andthe types of actions those participants may take and the informationthey can view. Using improved technological mechanisms, the electroniccommunication session can be used to prevent information leakage usingtechnological solutions including stack software object that controlsthe participation levels of the parties.

In some embodiments, an exemplary inventive electronicexecution-mediating platform of the present disclosure is configuredbased at least in part on an exemplary inventive electroniccommunicating protocol that allows users of the exemplary inventiveelectronic execution-mediating platform to electronically trade variousfinancial instruments such as, without limitation, corporate bonds. Insome embodiments, the exemplary inventive electronic execution-mediatingplatform of the present disclosure is configured based at least in parton the exemplary inventive electronic communicating protocol that allowsusers to utilize one or more inventive specifically-programmed graphicaluser interfaces (specialized GUIs) to engage in an innovativeelectronic-based competitive bidding of the present disclosure (theinventive electronic request for execution process (electroniccommunication process)) for one or more particular trading offersinvolving one or more financial instruments such as corporate bonds. Insome embodiments, the exemplary inventive electronic execution-mediatingplatform of the present disclosure is configured based at least in parton the exemplary inventive electronic communicating protocol that modelsthe trading workflow to be operated through messages that follow theFinancial Information Exchange Protocol (FIX) standard.

In some embodiments, the FIX standard includes an electroniccommunications protocol for international real-time exchange ofinformation related to securities transactions and markets. FIX includesstandards for message encodings and session protocols that may beadapted for use with the electronic communication session of theexemplary inventive electronic execution-mediating platform of thepresent disclosure.

In some embodiments, the FIX standard includes use of the FIX Adaptedfor Streaming (FAST) protocol. The FAST protocol facilitates one tocompress data organized in accordance with FIX protocol (“the FIX data”)to minimize the size of the transported FIX data.

FIGS. 1A and 1B show illustrative flow charts of an exemplary workflowassociated with a lifecycle of an exemplary electronic communicationsession having the technological improvement including the stacksoftware object.

In some embodiments, a user may initiate an electronic communication orexchange session on the electronic execution-mediating cloud platform toinitiate communication amongst parties. The initiating user may initiatethe communication session to exchange sensitive information and data.Thus, the electronic execution-mediating cloud platform may employ thestack software object to ensure that access to this sensitiveinformation is restricted to a minimum number of parties, each having arestricted ability to participate in the communication session accordingto participation levels instituted by the stack software object.

In some embodiments, the user may initiate the electronic communicationsession by selecting an intermediate entity (such as a dealer) from apool of potential intermediate entities. The intermediate entity isselected to mediate the electronic communication session. For example,in some embodiments, an initiating user (e.g., a buy-side initiatinguser) may be presented with a list of the intermediate entities withwhom the initiating user has the necessary agreements for participationin the electronic communication session. Thus, in some embodiments, theelectronic execution-mediating cloud platform may utilize an initiatinguser account and the intermediate entity account of each intermediateentity in the pool to identify matching intermediate entities. Thus, theelectronic execution-mediating cloud platform ensures that theintermediate entity ultimately selected by the initiating user has legalor other responsibilities to the initiating user, thus preventingleakage of information regarding the initiated electronic communicationsession to unaffiliated intermediate entities in the pool of potentialintermediate entities.

For example, the electronic execution-mediating cloud platform may onlysurface intermediate entities from the pool of potential intermediateentities that have matching agreement attributes to the initiating user,such as flags or data entries representing a legal agreement to engagewith each other in, e.g., financial asset trading. For example, theelectronic execution-mediating cloud platform may only surfaceintermediate entities that have legal agreements as dealers to theinitiating user to allow trading through the electronicexecution-mediating platform and network.

In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure may beconfigured, via the one or more exemplary specialized GUIs of thepresent disclosure, to allow the initiating user to electronicallyselect a desired intermediate entity (e.g., a single dealer). In someembodiments, the exemplary inventive electronic execution-mediatingplatform of the present disclosure may be configured to dynamicallyselect for the initiating user the intermediate entity (e.g., a singledealer) to be used. In some embodiments, the exemplary inventiveelectronic execution-mediating platform of the present disclosure may beconfigured to dynamically suggest, based at least in part on one or morespecialized analytical techniques (e.g., machine learning algorithms)detailed herein, to the initiating user a list of candidate intermediateentities (e.g., dealers) so that the initiating user can choose adesired intermediate entity (e.g., a single dealer) to be used.

The initiating user may select a single intermediate entity tofacilitate the session, which may trigger an electronic message to besent to the selected intermediate entity. The selected intermediateentity may receive a notification through, e.g., an electronicexecution-mediating application (e.g., the request to host an electroniccommunication session may appear as an “alert”, in a dedicated alertwindow, e.g., to minimize the amount of screen space used).

In some embodiments, the electronic execution-mediating application mayinclude, e.g., a software application, including front end design andback end processing, network communication, visualization generation anda graphical user interface for interacting with the electronicexecution-mediating cloud platform. In some embodiments, the electronicexecution-mediating application may include, e.g., an intermediateentity selection component enabling the initiating user to select anintermediate entity from the list of potential intermediate entitiessurfaced by the electronic execution-mediating cloud platform. Moreover,the electronic execution-mediating application may include, e.g., anintermediate entity component that enables a selected intermediateentity to accept, deny, and/or negotiate terms of the selection tomediate the electronic communication session. Additionally, theelectronic execution-mediating application may include an activitymonitor that enables the initiating user, the selected intermediateentity, or both to monitor activities occurring within the electroniccommunication session, such as, e.g., submissions, communications,transfers of information, and other data.

In some embodiments, the electronic message to the selected intermediateentity may send the electronic communication session data, includingelectronic communication session attributes to the selected intermediateentity. In some embodiments, the session attributes may includeparameters of a financial transaction, such as, e.g., committed level,trade now level, electronic communication session length (e.g., intime), spot instructions, among other parameters for a trade negotiationfor the trade of a quantity of a position in a financial instrument,such as, e.g., a bond, a stock, or other financial instrument. In someembodiments, the “trade now level” refers to, e.g., a pre-determinedprice for parties to the electronic communication session to purchaseposition in the financial instrument, or other minimum level of responsein exchange for a transfer of data. Each of the attributes defineaspects of what parties may participate in the electronic communicationsession and the rules concerning participation. The intermediate entitymay have an interest in less strict rules to include more parties forgreater confidence in, e.g., executing a trade or transfer during thesession, while the initiating user may have an interest in more strictrules to prevent information leakage. Thus, the platform enables anegotiation for a balance between potential information leakage andpotential mediation success. Thus, the electronic communication sessionmay be customized in every instance.

In some embodiments, the intermediate entity, via the electronicexecution-mediating application, may counter the session request tonegotiate the attributes. For example, the intermediate entity maycounter, e.g., to buy the subject of the transaction directly from theinitiating user (e.g., buy the financial instrument, e.g., bonds), tonegotiate the transaction spread, to negotiate the committed level, orother parameters of the transaction.

In some embodiments, the attributes may include, e.g., a backstop bidbetween the selected intermediate entity and the initiating user for aposition in a financial instrument subject to the electroniccommunication session. The backstop bid is an element of the pre-tradeinitiation process between the intermediate entity and the client whoselected him to host the electronic communication session. For example,where the initiation user is a seller that approaches an intermediateentity acting as a dealer to sell a financial instrument at a givenprice, the intermediate entity may commit to buy a lesser quantity ofthe financial instrument (e.g., a quantity of a bond below the quantitythat the seller is seeking to sell) and work an order on the rest. Thus,the seller gains certainty that at least some of the seller's positionin the financial instrument will be sold, while the intermediateentity's trade, e.g., once it prints on TRACE, may help other investorsassess the current value of the bond. Thus, in an electroniccommunication session, the backstop is a commitment by the intermediateentity to step in and buy up to the amount specified at the committedlevel, if the full amount fails to trade. The existence or absence of abackstop bid does not impact the ability of other investors to bidwhatever they choose. It may be implemented as an optional, one-timecommitment from the intermediate entity to cover a portion of the tradeif necessary.

In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured,via the one or more exemplary specialized GUIs of the presentdisclosure, to allow the initiating user to set one or more attributesfor which a particular participant or invitee to the electroniccommunication session would be able to see bids/offers placed during theexemplary electronic communication session by other invitees orinvitees; thus, increasing transparency associated with electronic tradeexecution, and the likelihood of price improvement for the initiatinguser.

In some embodiments, the initiating user may respond to intermediateentity counter with updated parameters. For example, the initiating usermay set new transactions spread as part of the new electroniccommunication session, or a new committed level or electroniccommunication session length, or any other parameter countered by theselected intermediate entity. However, where the counter by the selectedintermediate entity includes an offer to buy the position in financialinstrument directly, the initiating user may be presented with a tradeconfirm option, selectable by the initiating user to conduct the trade.

In some embodiments, once the attributes are agreed upon by theinitiating user and the selected intermediate entity, the initiatinguser may be provided with an option to instruct the intermediate entityduring the electronic communication session by sending electroniccommunications with a “Not to Trader” option. In some embodiments, theelectronic execution-mediating application may also present theinitiating user with an option to cancel or withdraw from the electroniccommunication session prior to launch of the electronic communicationsession.

In some embodiments, prior to launch of the electronic communicationsession, the electronic execution-mediating application may provide theselected intermediate entity with, e.g., an option to select inviteesfrom a pool of potential invitees and then launch the electroniccommunication session. In some embodiments, to select the invitees, theintermediate entity selects the alert/activity monitor item, the “tradeticket” appears and the customer recommendation list appears in theticket. At this point, the intermediate entity can select/deselectinvitees in the list or set filters based on the customer liquidityscores (likelihood to trade) and/or limit the total number ofinvitations sent out, e.g., to address information leakage. In someembodiments, once the selections are made, the intermediate entity“submits” the ticket and similar notifications are sent to the selectedinvitees.

In some embodiments, the selected intermediate entity may be permittedto select invitees from all electronic execution-mediating-enabledcounterparties. However, where the electronic execution-mediatingplatform includes larger quantities of potential invitees, theelectronic execution-mediating application may be configured to allowthe selected intermediate entity to set defaults for, e.g., the minimumliquidity score/maximum number of potential invitees to engage in thetrade. Thus, in some embodiments, the electronic execution-mediatingapplication may utilize invitee characteristics from potential inviteeaccounts. In some embodiments, the invitee account characteristics mayinclude, e.g., the invitee's name, the invitees committed level, and atransfer history associated with the invitee's account.

In some embodiments, the liquidity score and invitee limit fields existon the session request. Where the intermediate entity may set defaultson these limit fields, the intermediate entity may dynamically set andmodify the limit fields to dynamically filter potential invitees. Insome embodiments, in addition to the machine learning inviteesuggestions described above, the liquidity cloud analytics module mayalso track “concurrent activity” for each potential invitee, where theconcurrent activity indicates that a potential invitee is unavailable toparticipate in the electronic communication session. In someembodiments, the capacity to trade based on availability may vary basedon invitee characteristics, such as, e.g., the size of a firm. This isbecause larger firms may automate much of electronic communicationsession trade process, allowing the large firms to handle a highervolume of requests. Smaller firms, on the other hand, may manuallyparticipate via the stack GUI and thus likely have more limited capacityto participate in concurrent electronic communication sessions. Forexample only, there may be situation in which a smaller firm is unableto participate in an electronic communication session due to itsparticipation in other electronic communication or other tradingsessions occurring at the same time.

Moreover, the intermediate entity may be provided with a list ofsuggested invitees of the potential invitees, e.g., based on the accountcharacteristics associated with each potential invitee. For example, theliquidity cloud analytics module described below may predict alikelihood of each potential invitee to accept the parameters agreedupon by the initiating user and the selected intermediate entity, and toplace a winning bid for the transaction. In some embodiments, the listincludes the potential invitees that meet a liquidity threshold. In someembodiments, the liquidity threshold may include, e.g., a percentilethreshold (e.g., the invitee must be a member of certain percentile oflikelihood, such as, top 75 percentile, of the predicted likelihood ofpotential invitees to participate and/or win the transaction of theelectronic communication session), a number of invitees threshold (e.g.,a highest number of potential invitees according to predictedlikelihood, such as the top 10 or 20 invitees), or a likelihoodthreshold include a minimum predicted likelihood score.

In some embodiments, the initiating user may monitor the electroniccommunication session via the activity monitor and take steps to improvechances of execution. For example, the initiating user may, e.g., addmore invitees, edit committed level (for unlocked/open stacks), extendelectronic communication session length, or modify other attributes toimprove the likelihood of the execution of a trade. In some embodimentsthe activity monitor may provide a live or real-time update to theactivity monitor for the initiating user to monitor the state of theelectronic communication session. For example, the initiating user maymonitor the bids and choose to trade early or receive trade confirmationif electronic communication exchanges are executed according to theattributes.

In some embodiments, the set of session invitees selected by theintermediate entity may be presented with the activity monitoryaccording to the attributes of the electronic communication sessionagreed upon by the initiating user and the intermediate entity. In someembodiments, the attributes may define settings of the stack softwareobject that establish participation levels of selected invitees. In someembodiments, the participation levels affect the user computing devicesand the electronic execution-mediating cloud platform to restrict typesof activities that the parties to the electronic communication sessionmay engage in and view, e.g., via a stack graphical user interface(GUI). In some embodiments, the permissions of each session invitee toaccess communications during the electronic communication session, suchas, e.g., an open, locked or unlocked permissions level of the stackGUI, as described above.

In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured toallow each initiating user to set one or more attributes to establishthe settings of the stack software object that controls the scope ofwhat invitees could see at the stack GUI during an electroniccommunication session. For example, once the electronic communicationdealer has invited a select number of invitees, the electroniccommunication dealer's invitees and, optionally, the liquiditydatabase's invitees are able place bids or offers, depending on thedirection of the particular electronic communication session, for theselected financial instrument/security For example, in some embodiments,invitees to the electronic communication session are able to see certaininformation at the stack GUI based, at least in part, on whether thestack software object setting for the electronic communication sessionis “locked”, “unlocked,” or “open.” For example, in some embodiments,the initiating user and the electronic communication dealer can also seesome or all information being posted to the stack GUI. For example, insome embodiments, all invitees are able to bid/offer or improve thelevel of their bid/offer for the selected corporate bond for theduration of the electronic communication session. For example, in someembodiments, responding with price levels is a competitive process forthe duration of the electronic communication session and will bedetermined by price, then size, then time, unless the electroniccommunication session is fully allocated, at which point the submissionsshown by the stack GUI will be ordered by price, then time.

In some embodiments, when the stack GUI is locked the stack GUI orinvitees' submissions during the electronic communication session arevisible only to those invitees that have placed a bid having certainpredetermined parameter(s) (e.g., submissions at the committed level orbetter).

In some embodiments, when the stack GUI is unlocked the stack GUI orinvitees' submissions during the electronic communication session areonly visible to those invitees that have a placed a bid (e.g.,submissions at any level). For “unlocked” stacks, the initiating userspecifies predetermined parameters including, e.g., an indicative price(not firm). As with locked stacks, invitees need to submit levels at orbetter than the reserve level to gain access to the stack. Thedifference is that the initiating user has an option, not a firmcommitment, to trade at the reserve level.

In some embodiments, when the stack GUI is Open: the stack GUI orinvitees' submissions during the electronic communication session arevisible to all invitees.

In some embodiments, the stack type options help an initiatinguser/trader manage the trade-off for the amount of information they needto share with counterparties to create a robust bidding environment.

For example, in any given month, it may be that less than, e.g., 20% ofall US corporate bonds have at least one trade every business day. Thisepisodic nature of bond trading makes it difficult for investors tocalibrate the current price of a specific bond to the dynamic conditionsin the market. In some embodiments, the electronic execution-mediatingplatform employs the locked, unlocked, and open stack software objectparticipation levels to give trade initiating users tools to help themtailor the trading process across a range of market conditions.

In some embodiments, for liquid bonds from popular issuers, lockedstacks are generally the most appropriate option. The locked stackparticipation level forces invitees to commit some degree of risk inorder to see the prices submitted by those with whom they would becompeting to trade the bond.

In some embodiments, an unlocked stack gives the initiating user theoption to set a secondary level which if met by the bidders allows themto see the bids entered by others. The bidders' levels are treated as“firm” (i.e., must be honored), while the initiating user retains anoption to proceed or walk away from the trade. The unlocked stackparticipation level lets potential counterparties interact in a mannerthat can get a conversation started.

The open stack participate level creates a forum for price discovery for“hard to trade” assets.

In some embodiments, session invitees are then able to submit theirtrade parameters (size and level) and view the “stack”, which providestransparency into the activity of all participants. However, based onthe participation levels, the session invitees may or may not be able tosee identifiers (e.g., names) associated with each other session inviteeparticipating the electronic communication session. For example, alocked stack GUI may anonymize each session invitee in the electroniccommunication session.

In some embodiments, the session invitees may also be presented with theoption to submit one or more bids via the stack GUI. In someembodiments, a matching engine that processes inputs to an order bookanonymizes the names of the participants in the electronic communicationsession. The system also maintains the anonymized name mapped to eachparticipant. As the subscription manager routes updates about the orderbook to the stack GUI, it converts the anonymized name to the “My Bid”for display to the user. In some embodiments, communications to theelectronic communication session may be routed via the FIX standards andconverted in a similar fashion.

For example, in some embodiments, the electronic communication systememploys the “liquidity cloud”, e.g., the computing cloud 1705 of FIG. 17described below. In order to participate in the liquidity cloud, onemust be an intermediate entity on the platform or a client of at leastone intermediate entity. The liquidity cloud allows participants toshare their current interest with the electronic execution-mediatingplatform so that they have an ability to participate in trades, shouldthe opportunity present itself. As mentioned above, information leakagemay lead to a reluctance among market participants to advertise theirintentions. Thus, the liquidity cloud collects these intentions byelectronic communication session system, with the understanding thatshould the “other side of the trade” emerge, the electroniccommunication session system has the ability to reach liquiditythroughout its entire network.

In some embodiments, the stack software object controlling an electroniccommunication session system implements the rules that controls accessto content for all participants based upon their role in the trade. Theinitiating user only selects the intermediate entity. In some aspects,an initiating user may be permitted to communicate white list/black listrosters of other account managers that the initiating user wants toallow/block from invitation to the electronic communication session.Such an implementation can be a further means for controllinginformation leakage. For example only, it may be obvious to marketparticipants that if a given bond is being sold in a large size that, itis likely that, e.g., a top 10 asset manager is the seller, which mayinfluence the market.

In some embodiments, the attributes may only allow multiple bids, whichmay be scaled or independent, and bids, once placed, to only be allowedto improve on past bids. In some embodiments, the stack software objectcontrolling the electronic communication session may enable a bidder tospecify a minimum bid fill amount. In some embodiment, the electronicexecution-mediating application may only allow for bids to be cancelledby the intermediate entity. Moreover, where if the bid is allocated, thebidder may be presented with a trade confirm at the end of theelectronic communication session. However, if a bidder is outbid, thebidder may be presented with an alert. Furthermore, in some embodiments,a reset period may be established where a new bid that impacts existingallocations may trigger the reset period to facilitate resetting theallocations.

In some embodiments, the stack software object may enable the inviteesto have the option to remain anonymous until the electroniccommunication session is completed and ready for booking (if the inviteebids but is not part of the final trade allocation, the intermediateentity will not know they were bidding). In some embodiments, if noagreement exists between a winning bidder from the liquidity cloud andthe host intermediate entity, a “step-in intermediate entity” isassigned to settle the trade. Again, the invitee is in a position toparticipate at all because the invitee has a connection to at least oneintermediate entity (but not necessarily the intermediate entity thatwas selected).

In some embodiments, the one or more exemplary specialized GUIs of thepresent disclosure may be configured to allow users to engage in theexemplary inventive electronic communication process by allowing aseller or buyer of financial instruments/securities (e.g., corporatebonds) to send an electronic request that would request, via a computersystem, an intermediate party (e.g., a dealer) to host one or morecompetitive electronic executions via the electronic communicationsession (one or more particular electronic communication sessions forone or more particular financial instruments such as corporate bonds).For example, the exemplary inventive electronic execution-mediatingplatform of the present disclosure is configured to allow a singleintermediate entity (e.g., a single dealer) to execute the exemplaryelectronic communication bidding on behalf of a seller or a buyer byelectronically offering, via specialized electronic communicationmessaging, to a plurality of potential invitees an opportunity to fulfilthe initiating user's submission based on one or more suitable matchingcondition(s) whose illustrative examples are provided herein.

In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured torank potential counterparties by their anticipated level of interest ina transaction. This empowers those sending out these communications tolimit “information leakage”, thereby preventing providing an adversarywith information that can be used against a party (e.g., the initiatinguser or the invitees).

In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured toallow the intermediate entity to electronically select invitees to whomelectronic invitation will be electronically transmitted. In someembodiments, the exemplary inventive electronic execution-mediatingplatform of the present disclosure is configured to suggest, based atleast in part on one or more specialized analytical techniques (e.g.,machine learning algorithms) detailed herein, to the intermediate entity(e.g., the single dealer) a list of clients of such intermediate entity(potential invitees) who would be most likely to be interested inselling/buying one or more specific financial instruments (e.g.,specific corporate bond(s)) that would be part of the exemplary sell orbuy offer; consequently, allowing to electronically aggregate (andpotentially increase) liquidity across multiple invitees.

In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured torequire, via the one or more exemplary specialized GUIs of the presentdisclosure, an initiating user to submit a firm buy/sell order having aminimum price that, if met, the initiating user will commit to buy orsell the specified financial instrument (e.g., corporate bond),depending on the direction of the exemplary inventive electroniccommunication. In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured torequire, via the one or more exemplary specialized GUIs of the presentdisclosure, not only a certain price but also certain size/quantity(e.g., a minimum size/quantity). In some embodiments, the exemplaryinventive electronic execution-mediating platform of the presentdisclosure may be configured to vary value(s) for the required price,the required quantity, or both, based at least in part on one or more ofa type of financial instrument, issuer of financial instrument (e.g.,corporate bond), characteristic(s) of initiating user (e.g., historicaltransactional data), characteristic(s) of the selected intermediateentity (e.g., the single dealer), etc.

In some aspects, a matching engine in the computing environment canprocess inputs to anonymize the names of the participants in a trade.The system also maintains the anonymized name mapped to eachparticipant. As the subscription manager routes updates about the orderbook to the UI, it converts the anonymized name to the “My Bid” fordisplay to the user. In some embodiments, a similar conversion is madefor messages routed via FIX. In some embodiments, the dealer seesexplicit names for all participants because each is a client of dealer.However, the participants are prevented from view the names of otherparticipants to prevent information leakage. In some embodiments, theanonymized names are randomly chosen for each transaction. As a result,a buyer might be “bidder 1” on one trade and “bidder 89” on the nextone.

In some embodiments, the exemplary inventive electronicexecution-mediating platform of the present disclosure is configured toutilize one or more suitable specialized analytical techniques (e.g.,machine learning algorithms), as detailed herein, to identify mostlikely invitees to participate in the electronic communication session.For example, electronic execution-mediating platform may employ acloud-based analytics module that identifies the most likely invitees ofthe pool of potential invites to transact with a particular financialinstrument (e.g., corporate bond) based at least in part on continuouslyupdatable database(s) (liquidity database(s)) which electronicallyaggregate(s), for example, without limitation, one or more oftransactional data various market participants, their pre-tradeintentions, and characteristic(s) of their desired counterparty(ies). Insome embodiments, the exemplary inventive electronic execution-mediatingplatform of the present disclosure is configured to operate as amembership network of users who may be invited to participate in aparticular electronic communication session based on their pre-tradeintention and/or estimated trade likelihood even when they are notassociated with the particular intermediate entity (e.g., the singledealer); thus providing additional source of liquidity for the inventiveelectronic execution of the present disclosure. In some embodiments, theexemplary inventive electronic execution-mediating platform of thepresent disclosure is configured to utilize one or more suitablespecialized analytical techniques (e.g., machine learning algorithms),as detailed herein, to determine a liquidity score (a quantitativerepresentation of a likelihood to find a successful matching) for aparticular financial instrument (e.g., corporate bond) based at least inpart on, without limitation, users' pre-trade intentions and/or theirestimated trade likelihood.

In some embodiments, the exemplary liquidity cloud's analytics modulemay be configured to generate/utilize separate prediction models foreach intermediate entity and/or financial instrument (e.g., corporatebond) based on one or more machine learning technique (such as, but notlimited to, decision trees, boosting, support-vector machines, neuralnetworks, nearest neighbor algorithms, Naive Bayes, bagging, boosting,random forests). In some embodiments and, optionally, in combinationwith any embodiment described above or below, an exemplary neutralnetwork technique may be one of, without limitation, feedforward neuralnetwork, radial basis function network, recurrent neural network,convolutional network (e.g., U-net) or other suitable network. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an exemplary implementation of Neural Network may beexecuted as follows:

-   1) define Neural Network architecture/model,-   2) transfer the input data to the exemplary neural network model,-   3) transform the input data to a suitable numeric format (feature    engineering),-   4) train the exemplary model incrementally,-   5) determine the accuracy for a specific number of timesteps,-   6) save and apply the exemplary trained model to process the    newly-received input data,-   7) optionally and in parallel, continue to train the exemplary    trained model with a predetermined periodicity.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayspecify a neural network by at least a neural network topology, a seriesof activation functions, and connection weights. For example, thetopology of a neural network may include a configuration of nodes of theneural network and connections between such nodes. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the exemplary trained neural network model may also be specifiedto include other parameters, including but not limited to, biasvalues/functions and/or aggregation functions. For example, anactivation function of a node may be a step function, sine function,continuous or piecewise linear function, sigmoid function, hyperbolictangent function, or other type of mathematical function that representsa threshold at which the node is activated. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary aggregation function may be a mathematical function thatcombines (sum, product, etc.) input signals to the node. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an output of the exemplary aggregation function may beused as input to the exemplary activation function. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the bias may be a constant value or function that may be used bythe aggregation function and/or the activation function to make the nodelikely to be activated.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, an exemplary connection data for eachconnection in the exemplary neural network may include at least one of anode pair or a connection weight. For example, if the exemplary neuralnetwork includes a connection from node N1 to node N2, then theexemplary connection data for that connection may include the node pair<N1, N2>. In some embodiments and, optionally, in combination of anyembodiment described above or below, the connection weight may be anumerical quantity that influences if and/or how the output of N1 ismodified before being input at N2. In the example of a recurrentnetwork, a node may have a connection to itself (e.g., the connectiondata may include the node pair <N1, N1>).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayalso include a species identifier (ID) and fitness data. For example,each species ID may indicate which of a plurality of species (e.g.,intermediate entities', initiating users', invitees' and/or corporatebonds' categories) a particular model is classified in. For example, thefitness data may indicate how well the exemplary trained neural networkmodel models the input data set. For example, the fitness data mayinclude a fitness value that is determined based on evaluating thefitness function with respect to the model. For example, the exemplaryfitness function may be an objective function that is based on afrequency and/or magnitude of errors produced by testing the exemplarytrained neural network model on the input data set. As a simple example,assume the input data set includes ten rows, that the input data setincludes two columns denoted A and B, and that the exemplary trainedneural network model outputs a predicted value of B given an input valueof A. In this example, testing the exemplary trained neural networkmodel may include inputting each of the ten values of A from the inputdata set, comparing the predicted values of B to the correspondingactual values of B from the input data set, and determining if and/or byhow much the two predicted and actual values of B differ. To illustrate,if a particular neural network correctly predicted the value of B fornine of the ten rows, then the exemplary fitness function may assign thecorresponding model a fitness value of 9/10=0.9. It is to be understoodthat the previous example is for illustration only and is not to beconsidered limiting. In some embodiments, the exemplary fitness functionmay be based on factors unrelated to error frequency or error rate, suchas number of input nodes, node layers, hidden layers, connections,computational complexity, etc.

For example, a particular predictive model can be created by followingat least the 3-step process of:

1) Data Extraction, 2) Data Enrichment, and 3) Modelling.

Data Extraction

In some embodiments, the data used in the model comes from a combinationof information from private (non-public data of particular user (e.g.,intermediate entity)) and public electronic sources, and may include oneor more chosen from:

-   1) Transaction Data (e.g., the post-trade settlement process data),    having at least one or more of the following parameters:    -   a) Trade Time (dd/mm/yyyy hh:mm:ss)    -   b) CUSIP    -   c) Customer Account #    -   d) Trade Amount    -   e) Trade Price    -   f) Direction (buy/sell)-   2) Security/financial instrument (e.g., corporate bond) Master Data    (e.g., structural and descriptive attributes are collected for each    bond; for example, some of these attributes are used as direct model    inputs; and others are used to generate expected performance and    risk metrics that help the model calibrate the interactions between    clients and tradable bonds), having at least one or more of the    following parameters/fields:    -   a) Maturity Date,    -   b) Coupon Type,    -   c) Coupon Rate,    -   d) Coupon Payment Frequency,    -   e) Day-count type,    -   f) Call Schedule (if applicable),    -   g) Put Schedule (if applicable),    -   h) Floating Rate Coupon Formula (if applicable),    -   i) Principal Redemption Schedule (if applicable),    -   j) Issuer Name,    -   k) Industry/Sector (multiple levels),    -   l) Amount Outstanding,    -   m) Original Issue Amount,    -   n) Investment Grade/High Yield Flag,    -   o) Credit Ratings,    -   p) Spread data (e.g., to assess the market's view on the credit        risk of an asset versus credit ratings),    -   q) TRACE-eligibility flag,        -   i) Supplied by FINRA to identify bonds whose transactions            must be reported to the regulator.-   3) Price Data (e.g., a minimum of three months history may be    required to build/train each dealer's model; for example, the price    history repository covers all TRACE-eligible bonds and the benchmark    US Treasury notes/bonds that are a reference point for market    participants), having at least one or more of the following    parameters:    -   a) Daily closing prices are collected for each bond,    -   b) Intraday US Treasury prices are collected for each of the        following benchmark maturities:    -   c) 2-year, 3-yr, 5-yr, 7-yr, 10-yr, 30-yr,    -   d) A LIBOR model is used to derive the benchmark levels for        bonds with less than 13 months to maturity,    -   e) Trade-by-trade data from FINRA TRACE.

In some embodiments, the exemplary inventive analytics module mayutilize trader/salesperson identifier(s) in the trading data to segmentlarger asset managers into several distinct recommendation targets,based upon their sales coverage. For example, salesperson A may coverasset manager B for investment grade bonds but salesperson B may havethe relationship for high yield.

In some embodiments, the exemplary inventive analytics module mayutilize the account number to identify “block accounts”, which are usedfor the initial booking of a trade and “real accounts” that hold assetsfor investors. Then, the exemplary inventive analytics module mayidentify groups of “real accounts” that share similar asset selectionand trading tendencies, allowing to break down larger clients into moregranular segments. For example, groups of real accounts can beidentified based on, e.g., “passive investors” that seek to match theperformance of an index, and “active investors” look to beat the index.The bonds passive investors and active investors trade and the timing ofthese trades have distinctive patterns according to the type ofinvestor.

Data Enrichment

In some embodiments, the Data Enrichment step may involve a process ofTrade Aggregation, performed by the analytics module. In someembodiments, the Trade Aggregation process is component of ourhistorical Data Enrichment, where, e.g., analytics module extracts rawdata from settlement systems and matches the trades to market eventsreported in FINRA's TRACE system. The analytics module provides a viewof current market information, contributed by user firms (both buy-side& dealers). For example, since the post-trade data may include both theoriginal block trade and records for the final allocations to thebeneficial owners of the securities, the exemplary inventive analyticsmodule may generate a prediction model that would focus on decisionmaker(s) in each trade (e.g., the block trade counterparty). Forexample, using a combination of the dealer transaction data and theFINRA TRACE data, the exemplary inventive analytics module may match thetransactions from each dealer's records to the specific transaction inTRACE. For example, in cases where multiple transactions in the dealerrecords match a single TRACE trade, the exemplary inventive analyticsmodule may first look for a record that exactly matches the trade sizereported to FINRA. If such a trade is found, the exemplary inventiveanalytics module may associate the counterparty in that record as theexecuting counterparty. Furthermore, for example, the exemplaryinventive analytics module may create a parent/child relationshipbetween the executing counterparty and the other accounts that wereassociated with the trade.

In some embodiments, trade aggregation in the data enrichment step mayinclude, e.g., three data aggregation/normalization steps. In someembodiments, a first step includes block trade aggregation from thetransaction history files. The analytics module may create predictionsthat look to identify the entity that acted in the market to execute atrade. Institutional money managers may often trade on behalf of theirclients or, in the case of mutual funds, the legal entity responsiblefor the fund. Moreover, a single trade in the market is often allocatedto multiple accounts at the direction of the asset manager. As a result,the raw transaction records list the beneficial owner (buyer/seller) ofthe traded asset and may also contain a record reflecting the blocktrade recorded prior to the allocations. In some embodiments, theanalytics module employs the main characteristics of the tradesextracted from the dealer records and reconcile these trades totransactions reported in FINRA's TRACE feed. Matching is based uponexact matches on CUSIP, execution time, trade direction, price andcounterparty types. In some embodiments, the analytics module may alsolook to match on total trade size, although this is not always possibledue to FINRA's policy to cap the actual transaction amount reported fortrades larger than $5 MM for investment grade bonds and $1 MM for highyield bonds.

At times, an asset manager executes a trade with a dealer on behalf of asingle client. When this occurs, the asset manager's name may not appearin the trade record captured from the dealer. Instead, the recordcontains just the name of the beneficial owner. Accordingly, in someembodiments, a second step of trade aggregation may include mapping allthe beneficial owner accounts to the corresponding asset manager (acandidate for inclusion in our recommendation model, by virtue of theirtrade decision making authority). This process employs, e.g., aniterative analysis of the counterparties reported by the dealer wherethe rate of co-occurrence among the counterparties identified intransactions with multiple reported participants is measured. In someembodiments, the block account (i.e., the decision-making counterparty)is the most frequently observed trade counterparty.

Finally, the trade aggregation employs a number of natural languageprocessing (NLP) techniques to cleanse the dealer counterparty data. Insome embodiments, the raw information comes from content sets that havegrown organically, sometime over decades. Account names are created byadministrators at each client and there appear to be no validation rulesto enforce standardization. (This is name/address data that are notcritical to the trade processing performed). In some embodiments, theanalytics module may employ an official source for ID fields (Tax ID's,Legal Entity Identifiers (LEIS), or Market Place IDs (MPIDS)) as thetarget name for each entity in our process and use NLP pattern matchingto associate the text strings found in our sources to the appropriatefirm. As more users (e.g., dealers and buy-side users) are onboarded tothe platform, the analytics module is able to capture this informationdirectly from the dealers. In some embodiments, this mapping to IDfields allows identification of asset managers across the full spectrumof dealers on the platform.

In some embodiments, the first and second trade aggregation steps may beexecuted daily as new trade records are added in, e.g., a nightly modeltraining process. Thus, the client mappings may be refined as new namesand relationships are uncovered. However, other periods may be employed,such as, e.g., weekly or monthly updates, or in some embodiments, acontinuous update or live update process may be used.

In some embodiments, during the Data Enrichment step, for each record inthe dealer transaction table, the TRACE transaction table and theend-of-day price history table, the exemplary inventive analytics modulemay compute the following values:

1) Yield-to-worst (YTW) (e.g., Yield to the issuer's next logicalredemption date),

2) Option-adjusted Spread (OAS), 3) Option-adjusted Duration, 4)Option-adjusted Convexity, 5) Spread to Benchmark Treasury.

In some embodiments, during the Data Enrichment step, the exemplaryinventive analytics module may apply time series analysis to determineBaseline Liquidity Metric (liquidity score) based at least in part uponthe relative trade volume and trade count for each bond over the mostrecent three months of trading. It should be appreciated that otherperiods of time may instead be used as this value can be configurable.Additionally or alternatively, the Baseline Liquidity Metric (liquidityscore) can be based on multiple windows to time. In some embodiments,the Baseline Liquidity Metric are generated based on factors such as,e.g., the activity of the bond observable through TRACE. In someembodiments, for dealers, this is adjusted to account for his recentactivity, as a percentage of TRACE activity overall and in the specificbond being analysed, and for clients, the adjustment is derived from theoutput of the neural network, which projects the client's expected levelof interest in a particular trade opportunity. In some embodiments,Baseline Liquidity Metrics are a function of the desired trade price andthe size of the trade. The user has no way to influence how this3-dimensional surface is constructed, other than by executing trades. Insome embodiments, however, a user may modify the price and/or size of atrade, and the analytics module may return the score for thecorresponding point on this surface.

In some embodiments, during the Data Enrichment step, the exemplaryinventive analytics module may utilize Price/Yield volatility, by usingthe end-of-day prices for each bond, to compute, for example, withoutlimitation, 1-mo, 3-mo and 6-mo historical volatility for both the priceand yield. For example, in some embodiments, such metrics may provideinsight into the relative performance risk for bonds with similarstructural characteristics. In some embodiments, during the DataEnrichment step, the exemplary inventive analytics module may utilizeTotal Return metrics, using end-of-day prices for each bond, compute1-mo, 3-mo, and 6-mo realized returns for a unit investment. Forexample, these metrics present the models with critical performanceattributes for the assessing users' trading preferences.

Modelling

In some embodiments, during the Modelling step, the exemplary inventiveanalytics module may employ an ensemble modelling approach. For example,in some embodiments, the exemplary inventive analytics module may employNatural Language Processing to clean and normalize the set of eligibletrading counterparties. For example, in some embodiments, the exemplaryinventive analytics module may employ K-means clustering to createcandidate groupings of “comparable bonds”. For example, in someembodiments, the exemplary inventive analytics module may utilizeresults of these models as inputs to an exemplary neural network model(e.g., FIG. 3A) that would be trained to create an exemplary predictionmodel.

In some embodiments, cleansing and normalizing the set of eligibletrading counterparties ensures that the characteristics of past tradingbehaviours being quantified reflect the actual participants in themarket to provide value to the predictions in guiding future trades.

In some embodiments, K-means clustering is employed by the analyticsmodule due to greater interpretability. For example, the cluster membersmay be exposed in a GUI visualization, e.g., by depicting theclustering. However, other classification and clustering models may beused, including random forest, k-nearest neighbours, portioning, etc.

For example, in some embodiments, the exemplary inventive analyticsmodule may generate a user-related Master table/dataset/database. Forexample, the block-trade accounts identified in the trade aggregationprocess above may be mapped to a normalized account name usinginformation collected from the dealer's customer table. For example, insome embodiments, the exemplary inventive analytics module may use acombination of natural language processing and clustering technique(s)to automate the creation of these mappings.

In some embodiments, for example, large asset managers manage fundsusing a variety of investment strategies. Some funds employ passivestrategies, aiming to match the performance of some market benchmarkindex. Other funds employ active strategies that seek to beat theperformance of these indices. In the end, invitations to participate inan electronic communication are delivered to human traders, who managethe accounts that have passive or active performance goals. Treatingthese large firms as single entities is likely to result in asub-optimum model. For example only, at one extreme, an active manager'sinterest in bonds with premium prices may be negated by an equally largepassive fund manager who only trades bonds that trade at a discount topar.

From a modelling perspective, it may be a challenge to create thepartitioning of the accounts into the sub groups. The modelling processis not sensitive to the number of potential counterparties, providedthere is sufficient data to adequately model each group.

For example, in some embodiments, the exemplary inventive analyticsmodule may determine a Bond Similarity. For example, the exemplaryinventive analytics module may employ a K-means clustering model toestablish groupings based mainly on security master attributes. Forexample, each bond's assigned cluster number becomes an input featurefor the exemplary neural network of FIG. 3A.

For example, in some embodiments, as shown in FIG. 3A, the exemplaryinventive analytics module may input a set of transaction data andsecurity information, for example, collected over a minimum of threemonths, as input to the exemplary neural network. For example, suchinputs would represent known information that resulted in trades withspecific counterparties. For example, in some embodiments, the exemplaryinventive analytics module may utilize the exemplary neural network tofit a series of weights that produce a solution that maximizes thenumber of instances where the input from trade X identifies the actualbuyer in that trade.

For example, in some embodiments, the number of hidden layers depends onthe non-linear complexity of particular relationship(s) to be discoveredby the model. For example, in some embodiments, each hidden layer mayhave no more than ⅔ the number of nodes in the input layer. For example,in some embodiments, the exemplary inventive analytics module mayutilize a “SoftMax” function (e.g., a function that takes as input avector of K real numbers and normalizes it into a probabilitydistribution consisting of K probabilities) as the “activation function”applied at each node to control the transfer of information through thenetwork and to reach the output layer. For example, the model wouldassign the probability that each counterparty would be the buyer for thetrade described by a particular input sample.

For example, in some embodiments, the exemplary inventive analyticsmodule may utilize “Default_indicator” as a flag that indicates if abond has failed to meet its interest and or principal paymentobligations and to help refine the categorization of securities for boththe bond similarity and the neural network model generation/validation.

In some embodiments, an exemplary generated predictive model can betested by utilizing market data as input data. For example,counterparties in the output layer may be sorted by their probability ofexecution (e.g., the value of the “SoftMax” activation function). Forexample, a positive outcome is defined as cases where the observed bywas:

-   1) Present in the Top 10 most likely buyers, or,-   2) one of the Top 10 buyers bought the bond in the trade or a    substantially similar bond in the next 24 hours.

In some embodiments, the “substantially similar” test may be defined asa bond from the same cluster, using the K-means clustering model basedon bond attributes.

In some embodiments, the output layer of the neural network assigns avalue ranging from 0 to 1 to each candidate counterparty (names on adealer's client list) (CACi) that can be interpreted as the probabilitythat the counterparty will execute a trade with the specifiedattributes. The ranking is achieved by ordering the customers by thismetric.

In some embodiments, the metric above may be combined with themarket-level liquidity score derived from TRACE using the followingformula: LSi=(LSmarket+10*(CACi/2), 10) and presented to a user. Forexample, for bond X, LSmarket=7 and CACi=0.367. Thus, LSi=(7+3.67/2,10)=8.84, which may be rounded to 9.

In some embodiments, the above described formula is calibrated to actualmarket outcomes using the data from the actual electronic communicationprocess, e.g., through nightly, weekly, monthly, or real-time updatingand training.

In some embodiments, exemplary inventive analytics module may facilitatethe ability by an initiating user to access needed liquidity from more“natural” counterparties that an intermediate entity may select asinvitees to a given electronic communication session. This, once again,reduces information leakage to potential adversaries in the market. Insome embodiments, intermediate entities benefit from being able toprovide better service to both the initiating user, who is triggeringthe event, and to the invitees who are asked to participate in thesession. Intermediate entities struggle to provide adequate coverage toclients in the “long tail” of the customer list. The recommendationshelp highlight productive opportunities to engage these clients asinvitees to electronic communication sessions. In some embodiments,invitees may benefit similar to the intermediate entities because theinvitees are more often contacted for opportunities that matter to them,instead of calls meant to “keep them warm”.

FIG. 3B shows exemplary inputs into the exemplary inventive neuralnetwork of the present disclosure (e.g., as shown in FIG. 3A).

FIG. 4 shows exemplary testing of exemplary predictive models based ontraining and validation scores to determine when an optimal predictivemodel to be used by the exemplary inventive analytics module may existfor various analytics detailed herein.

FIGS. 5A-5C shows exemplary statistics utilized for training exemplarypredictive models.

FIG. 6 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure in a form of dashboard showing a state of theexemplary inventive liquidity cloud at a particular time.

FIG. 7 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure in a form of dashboard showing pre-trade intentionsof a particular user (e.g., initiating user) of the exemplary inventiveelectronic execution-mediating platform of the present disclosure in theexemplary inventive liquidity cloud at a particular time.

FIG. 8 is a screenshot of the exemplary inventive specialized GUI ofFIG. 7 with a pop-up inventive specialized GUI showing historical dataregarding a particular liquidity score.

FIG. 9 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure that a particular initiating user can use to set-upparameters of a desired electronic communication session (e.g., targetdealer(s), buy/sell indicator, corporate bond, amount, price, etc.).

FIG. 10 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure that a particular dealer can use to track request tohost electronic communication session(s).

FIG. 11 is a screenshot of an exemplary inventive specialized GUI of thepresent disclosure that a particular invitee can use to makesubmission(s) in to a particular electronic communication session.

FIGS. 12-16 are screenshots of various states of exemplary inventivespecialized stack GUIs of the present disclosure that are presented tothe initiating user (Seller), the electronic communication dealer, andan invitee (Bidder) during a particular electronic communicationsession. As FIGS. 12-16 illustrate, for example, invitee's ability toperform certain activities and view certain information may be limitedbased on parameters of the electronic communication session (Locked,Open, etc.).

FIG. 17 illustrates one embodiment of an exemplary environment in whichsome embodiments of the exemplary inventive electronicexecution-mediating platform of the present disclosure may operate. Insome embodiments, exemplary inventive electronic execution-mediatingplatform of the present disclosure may host a large number of users(e.g., at least 1,000, at least 10,000; at least 100,000; at least1,000,000) and/or is capable of conducting a large number of concurrenttransactions such electronic communication sessions (e.g., at least1,000; at least 10,000; at least 100,000; at least 1,000,000). In someembodiments, the exemplary inventive electronic execution-mediatingplatform of the present disclosure is based on a scalable computer andnetwork architecture that incorporates varies strategies for assessingthe data, caching, searching, and database connection pooling. Anexample of the scalable architecture is an architecture that is capableof operating multiple servers.

Referring to FIG. 17, another example computing environment 1700(similar to computing environment 100 discussed above) is illustrated,in which the exemplary inventive electronic execution-mediating platformof the present disclosure may be implemented. The computing environment1700 can include users utilizing computing devices 1702-1704 that arecapable of interacting (e.g., receiving and sending electroniccommunications) to and from another computing devices and computingsystems/architectures, such as Internet cloud 1705, servers 1706 and1707, each other, and the like. In some embodiments, the set of suchcomputing devices includes devices that connect using a wiredcommunications medium such as personal computers, multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PCs, and the like. In some embodiments, the set of suchcomputing devices also includes devices that typically connect using awireless communications medium such as cell phones, smart phones,pagers, walkie talkies, radio frequency (RF) devices, infrared (IR)devices, CBs, integrated devices combining one or more of the precedingdevices, or virtually any mobile device, and the like. In embodiments,each member device within member devices 1702-1704 may include a browserapplication that is configured to receive and to send web pages, and thelike. In embodiments, the browser application may be configured toreceive and display graphics, text, multimedia, and the like, employingvirtually any web based language, including, but not limited to StandardGeneralized Markup Language (SMGL), such as HyperText Markup Language(HTML), a wireless application protocol (WAP), a Handheld Device MarkupLanguage (HDML), such as Wireless Markup Language (WML), WMLScript,JavaScript, and the like. In embodiments, the disclosure is programmedin either Java or .Net.

In some embodiments, the computing devices 1702-1704 may be furtherconfigured to receive a message from another computing device employinganother mechanism, including, but not limited to email, Short MessageService (SMS), Multimedia Message Service (MMS), instant messaging (IM),internet relay chat (IRC), mIRC, Jabber, and the like.

In some embodiments, the exemplary network 1705 of the exemplaryinventive electronic execution-mediating platform of the presentdisclosure may be configured to have a cloud computing architecture. Forexample, the exemplary inventive liquidity cloud architecture may havethe exemplary Internet cloud architecture 1705 that may include servers1706-1707, running and/or in electronic communication with one or moredatabases such as database 1708 (e.g., liquidity database).

As used herein, the terms “Internet cloud,” “cloud computing,” “cloudarchitecture,” and similar terms correspond to at least one of thefollowing: (1) a large number of computers connected through a real-timecommunication network (e.g., Internet); (2) providing the ability to runa program or application on many connected computers (e.g., physicalmachines, virtual machines (VMs)) at the same time; (3) network-basedservices, which appear to be provided by real server hardware, and arein fact served up by virtual hardware (e.g., virtual servers), simulatedby software running on one or more real machines (e.g., allowing to bemoved around and scaled up (or down) on the fly without affecting theend user). FIGS. 18 and 19 illustrate schematics of exemplaryimplementations of the cloud computing/architectures that the exemplaryinventive electronic execution-mediating platform of the presentdisclosure may be configured as in accordance with at least someembodiments of the present disclosure. For example, the exemplaryinventive electronic execution-mediating platform of the presentdisclosure may be configured as, but not limiting to: infrastructure aservice (IaaS), platform as a service (PaaS), and software as a service(SaaS).

As briefly mentioned above, the electronic transaction platform of thepresent disclosure provides an RFX platform that provides users with acomputer based market for the buying and selling of financialinstruments (securities). The liquidity of financial instruments listedon the RFX platform can be increased at least in part due to the uniquefeatures of the electronic transaction platform itself. As noted above,these features include, but are not limited to, the RFX platform itselfand its associated workflow, the scoring functionality for financialinstruments to be traded, and the partner identification techniques thatcan identify the party(ies) most likely to result in a successful trade.

With particular reference to FIG. 6, an exemplary inventive specializedGUI 600 of the present disclosure is shown. The GI 600 can be presentedto a user (e.g., user 120) upon the user logging into or otherwiseaccessing the electronic transaction platform with her/his associatedcomputing device, e.g., computing device 104. The GUI 600 takes the formof a dashboard showing a state of the exemplary inventive liquiditycloud at a particular time and for a particular user. A Request Section610 of the GUI 600 can display the current outstanding requests fortrade of the particular user. A Response Section 612 of the GUI 600 candisplay the current outstanding responses of the particular user toother user's requests for trade. Finally, a Staged Order Section 630 canbe provided in the GUI 600, which displays various securities and theirassociated potential trade parameters.

The Staged Order Section 630 can include, e.g., a Cloud Score 631, anidentification of the security 633, the specificities 635 (Buy or Sell,Size/Quantity, Cost such as a spread, etc.) of a potential order, aLiquidity Score 637, and/or a Request Button 639. The Cloud Score 631can take the form of any representation (a number, letter grade,percentage, etc.) that identifies a likelihood of a trade of thesecurity based on based on expressed trading intentions by other users.Similarly, the Liquidity Score 637 can take the form of anyrepresentation (a number, letter grade, percentage, etc.) thatidentifies a likelihood of a trade of the security based on historicaltrading data of that security (and/or, of similar securities). In theillustrated example, the Cloud Score 631 and Liquidity Score 637 areintegers between 0-9, although other representations could be used. TheRequest Button 639 can be a quick select button that permits the user tobegin the RFX trading process for the specific security.

Now referring to FIG. 7, another exemplary inventive specialized GUI 600of the present disclosure is shown. The GUI 700 can be presented to auser (e.g., user 120) upon the user logging into or otherwise accessingthe electronic transaction platform with her/his associated computingdevice, e.g., computing device 104. The GUI 600 takes the form of adashboard showing pre-trade intentions of a particular user (e.g.,initiating user) of the exemplary inventive electronicexecution-mediating platform of the present disclosure in the exemplaryinventive liquidity cloud at a particular time. A Sell IntentionsSection 710 of the GUI 700 can display the current trading intentionsfor sales of securities for the particular user, and a Buy IntentionsSection 720 of the GUI 700 can display the current trading intentionsfor sales of securities for the particular user. Each of the Sell andBuy Intentions Sections 710, 720 can include e.g., an identification ofthe security 711, the specificities 713 (Buy or Sell, Size/Quantity,Cost such as a spread, etc.) of a potential order, a Liquidity Score715, a Cloud Score 717, and/or a Request Button 639. As mentioned above,the Liquidity Score 715 can take the form of any representation (anumber, letter grade, percentage, etc.) that identifies a likelihood ofa trade of the security based on historical trading data of thatsecurity (and/or, of similar securities). The Cloud Score 717 can takethe form of any representation (a number, letter grade, percentage,etc.) that identifies a likelihood of a trade of the security based onbased on expressed trading intentions by other users. In the illustratedexample, the Liquidity Score 715 and Cloud Score 717 are integersbetween 0-9, although other representations could be used. The RequestButton 719 can be a quick select button that permits the user to beginthe RFX trading process for the specific security.

A user can input her/his intention to trade a specific security, whichis then uploaded and analysed by the Liquidity Cloud described herein.With further reference to FIG. 8, a user can also update, cancel, orotherwise modify a previously input trade intention, which will resultin a notification 800, e.g., of the updated Cloud/Liquidity Score(s).The notification 800 can display to the user the change in theCloud/Liquidity Score(s) based on the modified trade intentions. Asdiscussed herein, the user intentions can be utilized by the electronictransaction platform of the present disclosure to determine theLiquidity Score 715 and/or Cloud Score 717 for a particular security,which can enhance the liquidity for the particular security.

With further reference to FIG. 9, another exemplary inventivespecialized GUI 900 of the present disclosure is shown. The GUI 900 canbe presented to a user (e.g., user 120) upon the user logging into orotherwise accessing the electronic transaction platform with her/hisassociated computing device, e.g., computing device 104. In someaspects, the GUI 900 can be presented to a particular user (e.g., aninitiating user) upon selection of a Request Button 719 to begin an RFXtrading process for a specific security. The GUI 900 permits aninitiating user 120 to select the potential trade parameters (buy orsell, quantity, committed level, time, etc.) for an RFX communicationsession. Once the initiating user 120 has input the desired tradeparameters, the user 120 can select the “Send RFX” button 910 that willbegin the RFX trading process for the specific security, e.g., bysending the trade parameters to a dealer.

Now referring to FIG. 10, an example GUI 1000 of the present disclosureis shown. The GUI 1000 can be presented to a user (e.g., user 120) uponthe user logging into or otherwise accessing the electronic transactionplatform with her/his associated computing device, e.g., computingdevice 104. In some aspects, the GUI 1000 can be presented to aparticular user who acts as an intermediate entity/dealer in the RFXplatform. In some aspects, a matching process can be utilized toidentify dealer(s) that are capable of acting as an intermediate entityfor the RFX communication session. For example only, the capabilities ofthe potential dealers can be matched with the identity of the initiatinguser, the desired trade parameters, the ability to trade the security,etc. The GUI 1000 permits a dealer to view any RFX communicationsession(s) that the dealer is currently hosting in a dashboard 1010. Thedashboard 1010 can include a listing of active RFX sessions 1012,recently completed RFX sessions 1014, and open invites to host an RFXsession 1015. Furthermore, and as described herein, the GUI 1000 caninclude a notification 1050 of a request to host an electroniccommunication session (such as that corresponding to the open invite1015), which may appear as an “alert” in a dedicated alert window.

Once a dealer has initiated an electronic communication session, thedealer can invite a plurality of other users (invitees) to participate.When a user participates in an electronic communication session, anexample of a GUI 1100 (FIG. 11) can be presented to the user (e.g., user120) upon the user logging into or otherwise accessing the electronictransaction platform with her/his associated computing device, e.g.,computing device 104. The GUI 1100 permits an invitee user to input anoffer to buy or sell by entering the trade parameters for the offer. Thetrade parameters can include, e.g., the quantity, spread, and fill type(all or none, or partial).

With reference to FIGS. 12-16, a plurality of GUIs are shownillustrating the various views with which an initiating user, anintermediate user (dealer), and an invitee user can be presented duringan example electronic communication session. The GUIs can be presentedto a user (e.g., user 120) upon the user logging into or otherwiseaccessing the electronic transaction platform with her/his associatedcomputing device, e.g., computing device 104. In the illustratedexamples, the initiating user is a seller of a corporate bond and theinvitee user is bidding to purchase the corporate bond from a dealer. Itshould be appreciated that this merely an example and in other scenariosthe initiating user can be a purchaser and the invitee(s) can be aseller of a security. For ease of description, however, the descriptionof FIGS. 12-16 may refer to the various users as “seller user,” “dealeruser,” and “bidder user.”

Referring now to FIG. 12, the initiating user can be presented with aGUI 1200 showing the current state of the electronic communicationsession. As described further herein, communications between the parties(dealers/intermediate entities, initiating users, and invitee users)will be coordinated during the electronic communication session, e.g,such that all parties may not be presented with the same information.The GUI 1200 can include a portion 1202 that identifies the tradingparameters of the electronic communication session as agreed upon by theinitiating user and the dealer. Furthermore, a portion 1204 can show thestatus of current bids in the electronic communication session. In someaspects, the GUI 1220 presented to the dealer can have the same (orsimilar components) as the initiating user. As shown in FIG. 12,however, the invitee user can be presented with a GUI 1240 that differsfrom GUIs 1200, 1220 depending on the configuration of the electroniccommunication session. An invitee user may be presented with a GUI 1240that includes the portion 1202 that identifies the trading parameters ofthe electronic communication session as agreed upon by the initiatinguser and the dealer, as well as a submit bid section 1242. The submitbid section 1242 permits the invitee user to enter a specific bid in theelectronic communication session.

As mentioned herein, the electronic communication session may requirethat an invitee user meet a specific bid level in order to see the bidsentered by other users. With additional reference to FIG. 13, the GUI1240 is shown with the invitee user having access to the portion 1204that shows the status of current bids in the electronic communicationsession. In the illustrated example of FIG. 13, the invitee user hassubmitted the best offer for the security and is in the “allocated”status of the electronic communication session. In FIG. 14, however, theinvitee user has been outbid and is in the “not allocated” status.Nonetheless, in some implementations, the invitee user can still see theportion 1204 that shows the status of current bids in the electroniccommunication session.

With reference to FIG. 15, the invitee user can update the previouslysubmitted bide via the submit bid section 1242. As shown in FIG. 15, theinvitee user has updated the bid from “+92” to “+90.5” in an effort toobtain the “allocated” status. Upon pressing the “Update Bid” button inthe submit bid section 1242, the invitee user can update the bid toreflect the current pricing. As shown in FIG. 16, the invitee user hasmoved into the top position of the electronic communication session andhas achieved “allocated” status. Furthermore, the electroniccommunication session has expired (“closed”) as shown in the tradingparameters portion 1202. Accordingly, the invitee user has beenallocated the security at the price and in the quantity offered, therebyachieving a successful trading session.

As mentioned herein, the liquidity score (such as Liquidity Score637/715) identifies a likelihood of a trade of the security based onhistorical trading data of that security and/or of similar securities.Essentially the liquidity score is a quantitative metric relating to theability to trade a security at a reasonable price, within a reasonableamount of time, and in a given quantity. There can be various ways tocalculate the liquidity score, as discussed more fully herein.

In some aspects, the liquidity score may be calculated based on thenumber of transactions and the volume of historical trades ofsecurities. For example only, the liquidity score may comprise acombination of a count ratio and a volume ratio for a given security ascompared to (all) other securities. In some implementations, a traceliquidity score can be determined based on the equation:

TLS_(i)=β_(i)+η_(i)

where TLS_(i) the trace liquidity score for a security i; β_(i) is countratio for a security i; and η_(i) is volume ratio for a security i. Thecount ratio β_(i) can be determined based on the equation:

$\beta_{i} = \frac{{Count}_{i}}{{Max}({Count})}$

where Count_(i) is the count of transactions for a security i; andMax(Count) is the count of transactions for the security that had themaximum number of transactions. Similarly, the volume ratio η_(i) can bedetermined based on the equation:

$\eta_{i} = \frac{Volume_{i}}{{Max}({Volume})}$

where Volume_(i) is the volume transacted for a security i; andMax(Volume) is the volume transacted for the security that had themaximum volume. A logarithmic transformation can be applied to the traceliquidity score in order to convert the distribution into a bell-shapedcurve. Further, the trace liquidity score can be normalized, bucketedinto a decile range, or otherwise converted to provide a relative scorethat is more easily understood.

In various alternative or additional implementations, the liquidityscore can be calculated based on a specific dealer used trade thesecurity. For example only, a security may have an increased chance ofbeing traded by a particular dealer if that particular dealer hasdisproportionally traded the security compared to the global market. Inorder to capture the influence of a dealer that has historically tradedthe security more frequently than the global market, a dealer score canbe calculated. The dealer score can be based on a local count ratio andlocal volume ratio. The local count ratio can be a measure of the numberof transactions performed by a particular (local) dealer versus thetotal number of transactions, and the local volume ratio can be ameasure of the volume transacted by a particular (local) dealer versusthe total volume transacted. In some implementations, the dealer scorecan be determined based on the equation:

${DS_{i}} = \frac{\beta_{Li} + \eta_{Li}}{2}$

where DS_(i) the dealer score for a security i; β_(Li) is the localcount ratio for a security i; and η_(Li) is the local volume ratio for asecurity i. The local count ratio β_(Li) can be determined based on theequation:

$\beta_{Li} = \frac{{Count}_{Li}}{{Count}_{i}}$

where Count_(Li) is the dealer (local) count of transactions for asecurity i; and Count_(i) is the total count of transactions for thesecurity. Similarly, the local volume ratio η_(Li) can be determinedbased on the equation:

$\eta_{Li} = \frac{Volume_{Li}}{Volume_{i}}$

where Volume_(Li) is the dealer (local) volume transacted for a securityi; and Volume_(i) is the total volume transacted for the security. Thedealer score can be combined with and/or used to augment the traceliquidity score to better capture a representation of the liquidityscore of a security. For example only, in one aspect both the traceliquidity score and the dealer score can be calculated for a particulardealer, and the higher score will be provided to the dealer. In thismanner, the general market liquidity for a security (trace liquidityscore) will be presented to the dealer unless the dealer score indicatesthat the liquidity for trading the security by the particular dealer isgreater than the general market (e.g., the dealer has been moresuccessful than the market as a whole).

Alternatively or additionally, the liquidity score can be calculatedbased on the price at which a security is desired to be traded. Thetrace liquidity score and dealer liquidity score described above relatemerely to the number and volume of completed transactions for asecurity. In order to capture the effect of price on the liquidity of asecurity, a pricing deviation score can be calculated. The pricingdeviation score can be based on a reference pricing number (such as theend of day pricing for a security or real time pricing indication) for arecently completed trade, the completed trade pricing for the security,and the desired pricing of the security. In some implementations, thepricing deviation score can be determined based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

where ΔLS(F(P_(i))) is the pricing deviation score for a security i;REFP_(i) is the reference price (end of day price or other price) forthe security i; STD_(i) is the average standard deviation of thereference price and trade price for the security i; and IP_(i) is theinput price for the security i at which the liquidity score iscalculated. Similar to the dealer score, the pricing deviation score canbe combined with and/or used to augment the trace liquidity score (withor without the dealer score or other augmentations) to better capture arepresentation of the liquidity score of a security. For example only,the pricing deviation score can be added/subtracted from the traceliquidity score or dealer score in order to determine the liquidityscore for a security at a certain input price.

In further implementations, the liquidity score can becalculated/adjusted based on a specific trade party for the security.For example only, a security may have an increased chance of beingtraded with a particular party if that particular party has showninterest in trading the security in the past. In order to capture theinfluence of such previous transactions, a customer specific liquidityscore can be calculated. The customer specific liquidity score can bebased on historical trading data for that security and/or one or moreother securities/financial instruments related to the security. In someimplementations, a machine learning model can determine a customer valuefor each specific customer that is indicative of the probability thatthe specific customer will execute a trade for the security at thespecified attributes. In some implementations, that customer value canbe between 0 and 1. As mentioned above, in some embodiments, thecustomer value may be combined with any of the liquidity scoresmentioned above (such as Liquidity Score 637/715) using the followingformula: LSi=(LSmarket+10*(CACi/2), 10), where LSi is the customerspecific liquidity score for customer i; LSmarket is the liquidity scorethat is being adjusted; and CACi is the customer value. As described inthe example above, for bond/security X in which LSmarket=7 andCACi=0.367, LSi=7, the customer specific liquidity scoreLSi=(7+10*(0.367/2), 10)=8.84, which may be rounded to 9.

As mentioned herein, the cloud score (such as Cloud Score 631/717)identifies a likelihood of a trade of the security based on expressedtrading intentions by other users. The cloud score is a quantitativemetric relating to the ability to trade a security at a given price, ina given quantity, at a specific point in time, based on tradingintentions expressed by users. Essentially, the cloud score reflects adegree of matching between an expressed trade interest of a user withcontra-interest of other users (match buy with sell, sell with buy).There can be various ways to calculate the cloud score, as discussedmore fully herein.

In some implementations, a matching score is determined for each ofprice and quantity for a security, e.g., on a 0 to 1 basis where κindicates no matching and 1 indicates a perfect match of the metric(price or quantity), for each particular party that has expressed atrading intention for the security. The matching score can be determinedbased on one or more features, such as historical information on tradevolume, trade sizes, trade types, and the like, that are used as inputsto a model (e.g., a machine learning model). For example only, anycombination of these features can be used as independent variable inputsinto a model and compared to historical trade outcomes as a dependentvariable, where the probability of a match is normalized to a valuebetween zero and one. The matching scores for each of the price andquantity metrics can be combined to determine a combined matching score.For example only, the matching scores for each of the price and quantitymetrics can be multiplied and the square root of their product can bethe combined matching score. In this manner, the combined matching scorecan be determined for each particular party that has expressed a tradingintention for the security. Further, in some aspects, the combinedmatching score can facilitate a ranked ordering of the parties.

Once the combined matching score is determined for each party, thesecombined matching scores can be aggregated, e.g., in a ranked ordering.In some implementations, the aggregation can be based on a subset of thehighest combined matching scores. For example only, the subset can bebased on: (i) a maximum number of parties (such as 5, 10, 15); (ii) atotal volume expressed for trading for the parties (such as a multipleof the volume to be traded); or (iii) a combination thereof. In thismanner, an aggregated cloud score can be determined that appropriatelyreflects the interest of parties in trading a security at the givenprice and in a specific quantity.

As one example of the above, if a party A desires to trade a quantity Xof a security at a price Y, a matching score is determined for bothquantity X and price Y for each particular party that has expressed acontra trading intention for the security (if party A wants to sell thesecurity, the contra intention would be parties wanting to buy thesecurity, and vice-versa). Once the matching scores for each contraparty and each of quantity and price are determined, a combined matchingscore can be determined for each contra party. The combined matchingscores for the contra parties can then be aggregated, e.g., based on amaximum number of contra parties and/or the quantity X. In someimplementations, the maximum number of parties can be ten and the volumeis limited to four times the quantity X. In this manner, a limitednumber of contra parties are identified from which the cloud score canbe determined. Other aggregation techniques could be utilized.

In some aspects, the combined matching scores for the limited number ofcontra parties can be combined to determine an overall cloud score. Forexample only, the cloud score can be an average of the combined matchingscores for the limited number of contra parties. Alternatively, thecloud score can be determined based on the equation:

CS_(i)=product(MatchScores[1:n])^(1/n)

where CS_(i) is the cloud score for a security i; n is the number ofcontra parties as limited; and product(MatchScore[1:n]) is the productof the combined matching scores for each of the n limited parties. Itshould be appreciated that other techniques for combining the variousscores can be utilized.

In some implementations, and with reference to FIGS. 12-16, theliquidity score, the cloud score, or the combination thereof can beutilized to rank securities/financial instruments in GUIs that arepresented to an initiating user, an intermediate user (dealer), and aninvitee user during an example electronic communication session. In someaspects, a ranked list of financial instruments can be generated basedon the liquidity/cloud scores described herein. Furthermore, as furtherdata is obtained which change the liquidity/cloud scores and theassociated rankings for the financial instruments shown in the GUI, thecomputing device may rearrange the images in the GUI to re-order thedisplayed ranked list to reflect the updated ranking.

A measure of bond or other security similarity may also be employed insome implementations in order to assist in determining a liquidity scoreor similar measure of liquidity of a security. A machine learningalgorithm can be utilized to model such security similarity. Asmentioned above, in some embodiments, an exemplary inventive analyticsmodule may determine a Bond Similarity. For example, the exemplaryinventive analytics module may employ a K-means clustering model toestablish groupings based mainly on security master attributes. Forexample, each bond's assigned cluster number becomes an input featurefor the exemplary neural network of FIG. 3A. The description of BondSimilarity above can also be generally applicable to any type offinancial instrument, including but not limited to bonds. In someimplementations, the identification of financial instruments that aresimilar to a particular financial instrument can be based on a clusteranalysis or other clustering process. For example only, the clusteranalysis may utilize the characteristics of a financial instrument(described herein) to group, match, or otherwise identify similarfinancial instruments, e.g., those financial instruments that havecharacteristics that match the characteristics of the financialinstrument being examined. Additional or alternative techniques fordetermining similar securities can also be used, as further describedherein.

The determination of securities similarity can, e.g., be performed in anunsupervised manner as there may be no one specific, definitive way tomeasure the error between a model's predictions and its label. In someaspects, a feature space for securities can be determined to assist inthe determination of security similarity. For example only, two bondshaving the same issuer, rating, and industry may be more closely relatedthan a bond from that issuer and a separate bond from an unrelatedissuer. In order to determine such security similarity, a set ofcharacteristics or features can be determined for comparison. The set offeatures can include any features considered to assist in determining asimilarity between securities. For example only, an expected return canbe utilized as one of the features to determine similarity betweensecurities (financial instruments). The expected return of a financialinstrument is a measurement of the anticipated profit or loss associatedwith the financial instrument and can be calculated or determined invarious ways. In some aspects, the expected return for a bond as thefinancial instrument/security can be equal to the coupon rate, thecurrent yield rate, the difference between the face value of the bondand the current price of the bond divided by the current price of thebond, or any combinations of these features. Furthermore, with respectto a bond as the security, the features can include one or more of thefollowing: Bond Maturity Date, Bond Issue Date, Bond Rating, CouponRate, Industry, a Rule 144A Flag (indicating whether or not the bondfalls under Rule 144A), whether the bond is callable or not, whether thebond is high yield or investment grade, the Issue Amount Outstanding,Coupon Type, Bond Ticker Name, Trade Datetime, Spread at Trade Time,Trade Amount, and Trade Counterparty. Various different types of machinelearning algorithms for modelling security similarity can be utilized.For example only, a clustering algorithm, a nearest neighbour, a K-DTree clustering model, a binary tree algorithm, or other form ofunsupervised machine learning model for determining a similarity scoreor similar objects can be utilized to determine security similarity.

In some aspects, it may be beneficial to limit the number of securitiesthat are compared to a particular security in order to determine asimilar security. For example only, a security that is transactedrelatively infrequently may be removed from the comparison process assuch securities may not have a liquidity score that is representativefor similar securities. Similarly, data related to trades by aninfrequent or low volume customer may be removed, as such data may notbe generally applicable and may, therefore, provide spurious results. Inyet another example, data related to trades performed via specialized ornot generally available trading partners or platforms may be removed.Such data may, in some cases, not be generally applicable to the marketas a whole and, therefore, may not provide good candidates fordetermining similar securities. It should be appreciated that otherforms of data filtering, as well as similarity scoring/determination canbe used and still remain within the scope of the present disclosure.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures. The figures are not drawn to scale and may be simplifiedfor clarity. It is contemplated that elements and features of oneembodiment may be beneficially incorporated in other embodiments withoutfurther recitation.

Among those benefits and improvements that have been disclosed, otherobjects and advantages of this present disclosure can become apparentfrom the following description taken in conjunction with theaccompanying figures. Detailed embodiments of the present disclosure aredisclosed herein; however, it is to be understood that the disclosedembodiments are merely illustrative of the present disclosure that maybe embodied in various forms. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments of the present disclosure may be readily combined, withoutdeparting from the scope or spirit of the present disclosure. Further,when a particular feature, structure, or characteristic is described inconnection with an implementation, it is submitted that it is within theknowledge of one skilled in the art to affect such feature, structure,or characteristic in connection with other implementations whether ornot explicitly described herein.

The term “based on” is not exclusive and allows for being based onadditional factors not described, unless the context clearly dictatesotherwise. In addition, throughout the specification, the meaning of“a,” “an,” and “the” include plural references. The meaning of “in”includes “in” and “on.”

At least some aspects of the present disclosure will now be describedwith reference to the following numbered clauses.

-   1. A method comprising:    -   receiving, by at least one processor, a session request from an        initiating user;        -   wherein the session request comprises an electronic            communication session over a cloud computing network for a            transfer of a quantity of a position in at least one            financial instrument from the initiating user to at least            one session invitee;    -   generating, by the at least one processor, a list of potential        intermediate entities based at least in part on a respective        dealer liquidity score associated with each potential        intermediate entity of the potential intermediate entities    -   receiving, by the at least one processor, a selection from the        initiating user identifying a selected intermediate entity of        the potential intermediate entities to mediate the electronic        communication session;    -   enabling, by the at least one processor, the initiating user and        the selected intermediate entity to negotiate attributes of the        electronic communication session;    -   generating, by the at least one processor, based on the        attributes of the electronic communication session, a stack        software object controlling a plurality of participation levels        in the electronic communication session for each selected        invitee of a set of selected invitees;        -   wherein the plurality of participation levels comprises:            -   i) a locked stack participation level,            -   ii) an unlocked stack participation level, and            -   iii) an open stack participation level;    -   receiving, by the at least one processor, an invitee selection        from the selected intermediate entity indicating the set of        selected invitees selected from a plurality of potential        invitees;    -   establishing, by the at least one processor, the electronic        communication session, associated with an intermediary computing        device of the selected intermediate entity;        -   wherein the electronic communication session comprises the            stack software object;    -   preventing, by the at least one processor, a respective invitee        computing device associated with each respective selected        invitee from accessing activities in the electronic        communication session unless the respective selected invitee        satisfies at least one first predetermined parameter based on        the locked stack participation level of the stack software        object;    -   enabling, by the at least one processor, an initiating computing        device associated with the initiating user to access in the        electronic communication setting at a reserve level while        preventing each respective invitee computing device associated        with each respective selected invitee from accessing the        activities in the electronic communication session unless the        respective selected invitee satisfies at least one second        predetermined parameter based on the unlocked stack        participation level of the stack software object; and    -   enabling, by the at least one processor, the initiating        computing device associated with the initiating user and each        respective invitee computing device associated with each        respective selected invitee to access the activities in the        electronic communication session based on the open stack        participation level of the stack software object.-   2. The method of clause 1, wherein the dealer liquidity score    comprises a calculated score assigned to each potential intermediate    entity for the electronic communication session based at least in    part on historical trading data of related financial instruments to    the at least one financial instrument;-   3. The method of clause 1, further comprising generating, by the at    least one processor, an alert to a respective user computing    associated with each respective selected invitee of the selected    invitees to enable each respective selected invitee to join the    electronic communication session according to the plurality of    participation levels.-   4. The method as recited in clause 1, wherein the dealer selection    GUI further enables the selected intermediate entity to buy the at    least a portion of the quantity of the position in the at least one    financial instrument from the initiating user prior to the    electronic communication session.-   5. The method as recited in clause 1, wherein the stack software    object provides options for each selected invitee of the set of    selected invitees to submit respective trade parameters comprising a    respective size and a respective level of a respective portion of    the position in the at least one financial instrument.-   6. The method as recited in clause 5, wherein the stack software    object prevents each selected invitee of the set of selected    invitees to submit respective trade parameters having lesser value    than previously submitted trade parameters.-   7. The method as recited in clause 1, wherein the stack software    object is configured to anonymize each selected invitee in the    electronic communication session-   8. The method as recited in clause 1, wherein the electronic    communication session remains active until the quantity of the    position in the at least on financial instrument is fully allocated.-   9. The method as recited in clause 1, wherein the plurality of    potential invitees comprises potential invitees having concurrent    activities below a threshold level of concurrent activity.-   10. The method as recited in clause 1, further comprising:    -   utilizing, by the at least one processor, a liquidity analytical        model to predict a respective value indicative of a respective        expected level of interest for each respective potential invitee        of a plurality of potential invitees based on characteristics of        the at least one financial instrument and transaction histories        of each potential invitee;        -   wherein each respective expected level of interest comprises            a respective probability of each respective potential            invitee to execute a trade of a financial instrument having            common attributes to the at least one financial instrument;            and    -   ranking, by the at least one processor, the plurality of        potential invitees according to each respective value indicative        of the respective expected level of interest.-   11. A system comprising:    -   at least one processor configured to:    -   receive a session request from an initiating user;        -   wherein the session request comprises an electronic            communication session over a cloud computing network for a            transfer of a quantity of a position in at least one            financial instrument from the initiating user to at least            one session invitee;    -   generate a list of potential intermediate entities based at        least in part on a respective dealer liquidity score associated        with each potential intermediate entity of the potential        intermediate entities    -   receive a selection from the initiating user identifying a        selected intermediate entity of the potential intermediate        entities to mediate the electronic communication session;    -   enable the initiating user and the selected intermediate entity        to negotiate attributes of the electronic communication session;    -   generate based on the attributes of the electronic communication        session, a stack software object controlling a plurality of        participation levels in the electronic communication session for        each selected invitee of a set of selected invitees;        -   wherein the plurality of participation levels comprises:            -   i) a locked stack participation level,            -   ii) an unlocked stack participation level, and            -   iii) an open stack participation level;    -   receive an invitee selection from the selected intermediate        entity indicating the set of selected invitees selected from a        plurality of potential invitees;    -   establish the electronic communication session, associated with        an intermediary computing device of the selected intermediate        entity;        -   wherein the electronic communication session comprises the            stack software object;    -   prevent a respective invitee computing device associated with        each respective selected invitee from accessing activities in        the electronic communication session unless the respective        selected invitee satisfies at least one first predetermined        parameter based on the locked stack participation level of the        stack software object;    -   enable an initiating computing device associated with the        initiating user to access in the electronic communication        setting at a reserve level while preventing each respective        invitee computing device associated with each respective        selected invitee from accessing the activities in the electronic        communication session unless the respective selected invitee        satisfies at least one second predetermined parameter based on        the unlocked stack participation level of the stack software        object; and    -   enable the initiating computing device associated with the        initiating user and each respective invitee computing device        associated with each respective selected invitee to access the        activities in the electronic communication session based on the        open stack participation level of the stack software object.-   12. The method of clause 11, wherein the dealer liquidity score    comprises a calculated score assigned to each potential intermediate    entity for the electronic communication session based at least in    part on historical trading data of related financial instruments to    the at least one financial instrument;-   13. The system of clause 11, wherein the at least one processor is    further configured to generate an alert to a respective user    computing associated with each respective selected invitee of the    selected invitees to enable each respective selected invitee to join    the electronic communication session according to the plurality of    participation levels.-   14. The system as recited in clause 11, wherein the dealer selection    GUI further enables the selected intermediate entity to buy the at    least a portion of the quantity of the position in the at least one    financial instrument from the initiating user prior to the    electronic communication session.-   15. The system as recited in clause 11, wherein the stack software    object provides options for each selected invitee of the set of    selected invitees to submit respective trade parameters comprising a    respective size and a respective level of a respective portion of    the position in the at least one financial instrument.-   16. The system as recited in clause 15, wherein the stack software    object prevents each selected invitee of the set of selected    invitees to submit respective trade parameters having lesser value    than previously submitted trade parameters.-   17. The system as recited in clause 11, wherein the stack software    object is configured to anonymize each selected invitee in the    electronic communication session-   18. The system as recited in clause 11, wherein the electronic    communication session remains active until the quantity of the    position in the at least on financial instrument is fully allocated.-   19. The system as recited in clause 11, wherein the plurality of    potential invitees comprises potential invitees having concurrent    activities below a threshold level of concurrent activity.-   20. The system as recited in clause 11, wherein the at least one    processor is further configured to:    -   utilize a liquidity analytical model to predict a respective        value indicative of a respective expected level of interest for        each respective potential invitee of a plurality of potential        invitees based on characteristics of the at least one financial        instrument and transaction histories of each potential invitee;        -   wherein each respective expected level of interest comprises            a respective probability of each respective potential            invitee to execute a trade of a financial instrument having            common attributes to the at least one financial instrument;            and    -   rank the plurality of potential invitees according to each        respective value indicative of the respective expected level of        interest.-   21. A computer-implemented method of hosting an electronic    communication session for trading a financial instrument between an    initiating user and one or more invitee users, comprising:    -   receiving, at a computing device having one or more processors,        a request to begin the electronic communication session from an        initiating user, the request identifying the financial        instrument to be traded and various terms at which the        initiating user is willing to trade the financial instrument;    -   identifying, at the computing device, one or more dealer users        capable of acting as an intermediate entity for trading the        financial instrument based on the request;    -   providing, from the computing device and to the initiating user,        a list of the one or more identified dealer users;    -   receiving, at the computing device and from the initiating user,        a selection of a particular dealer user based on the list of the        one or more identified dealer users;    -   receiving, at the computing device and from the particular        dealer user, a list of the one or more invitee users to        participate in the electronic communication session;    -   transmitting, from the computing device and to each of the one        or more invitee users, an invitation to participate in the        electronic communication session;    -   establishing, at the computing device, the electronic        communication session, wherein the electronic communication        session permits each of the one or more invitee users to submit        an offer to trade the financial instrument with the initiating        user, each offer comprising an identification of its associated        invitee user and trade parameters including a price and a        quantity of the financial instrument; and    -   selectively sharing, from the computing device, information        regarding offers received during the electronic communication        session with each of the initiating user, the dealer user, and        the one or more invitee users, wherein the initiating user will        receive the trade parameters for each received offer, the dealer        user will receive the identification of the trade parameters for        each received offer and its associated invitee user, and each        particular invitee user will receive either: (i) the trade        parameters for each received offer when the particular invitee        user has submitted its own offer, or (ii) no information        regarding the received offers when the particular invitee user        has not yet submitted its own offer.-   22. The computer-implemented method of clause 21, wherein the    identifying the one or more dealer users capable of acting as the    intermediate entity for trading the financial instrument comprises:    -   determining a liquidity score for the financial instrument based        on historical trading data for one or more other financial        instruments related to the financial instrument;    -   identifying historical dealers associated with the historical        trading data.-   23. The computer-implemented method of clause 22, wherein    determining the liquidity score for the financial instrument is    based on a machine learning model.-   24. The computer-implemented method of clause 23, wherein the    machine learning model determines the liquidity score based on a    number of historical transactions and a volume of historical    transactions for the financial instrument to be traded.-   25. The computer-implemented method of clause 24, wherein the    machine learning model determines the liquidity score based on a    count ratio of the number of historical transactions for the    financial instrument to be traded to a maximum number of historical    transactions for any financial instrument observed.-   26. The computer-implemented method of clause 25, wherein the    machine learning model determines the liquidity score based on a    volume ratio of the volume of historical transactions for the    financial instrument to be traded to a maximum volume of historical    transactions for any financial instrument observed.-   27. The computer-implemented method of clause 26, wherein the    machine learning model determines the liquidity score based on a    combination of the count ratio and the volume ratio.-   28. The computer-implemented method of clause 24, wherein the    machine learning model determines the liquidity score based on a    volume ratio of the volume of historical transactions for the    financial instrument to be traded to a maximum volume of historical    transactions for any financial instrument observed.-   29. The computer-implemented method of clause 21, wherein the    identifying the one or more dealer users capable of acting as the    intermediate entity for trading the financial instrument comprises:    -   determining a dealer specific liquidity score for the financial        instrument based on historical trading data for one or more        other financial instruments related to the financial instrument;    -   identifying historical dealers associated with the historical        trading data.-   30. The computer-implemented method of clause 29, wherein    determining the dealer specific liquidity score for the financial    instrument is based on a machine learning model.-   31. The computer-implemented method of clause 30, wherein the    machine learning model determines the dealer specific liquidity    score based on a number of historical transactions with a particular    dealer and a volume of historical transactions for the financial    instrument to be traded with the particular dealer.-   32. The computer-implemented method of clause 31, wherein the    machine learning model determines the dealer specific liquidity    score based on a dealer specific count ratio of the number of    historical transactions for the financial instrument to be traded    with the particular dealer to a maximum number of historical    transactions for any financial instrument observed with the    particular dealer.-   33. The computer-implemented method of clause 32, wherein the    machine learning model determines the dealer specific liquidity    score based on a dealer specific volume ratio of the volume of    historical transactions for the financial instrument to be traded    with the particular dealer to a maximum volume of historical    transactions for any financial instrument observed with the    particular dealer.-   34. The computer-implemented method of clause 33, wherein the    machine learning model determines the dealer specific liquidity    score based on a combination of the dealer specific count ratio and    the dealer specific volume ratio.-   35. The computer-implemented method of clause 31, wherein the    machine learning model determines the dealer specific liquidity    score based on a volume ratio of the volume of historical    transactions for the financial instrument to be traded with the    particular dealer to a maximum volume of historical transactions for    any financial instrument observed with the particular dealer.-   36. The computer-implemented method of clause 23, wherein the    machine learning model determines the liquidity score based on a    pricing deviation score corresponding to a difference between a    traded pricing and a desired pricing for the financial instrument to    be traded, the traded pricing comprising a previous price at which    the financial instrument was traded and the desired pricing    comprising a desired price at which the initiating user is willing    to trade the financial instrument.-   37. The computer-implemented method of clause 36, wherein the    previous price at which the financial instrument was traded    comprises an end of day price for the financial instrument.-   38. The computer-implemented method of clause 37, wherein the    pricing deviation score is based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

-   -   where ΔLS(F(P_(i))) is the pricing deviation score for a        financial instrument i; REFP_(i) is the reference pricing of a        recently completed trade for the financial instrument i; STD_(i)        is an average standard deviation of the reference pricing and        trade price for the financial instrument i; and IP_(i) is the        desired price for the financial instrument i.

-   39. The computer-implemented method of clause 21, wherein the    identifying the one or more dealer users capable of acting as the    intermediate entity for trading the financial instrument comprises:    -   determining a customer specific liquidity score for the        financial instrument based on historical trading data for one or        more other financial instruments related to the financial        instrument;    -   identifying historical dealers associated with the historical        trading data.

-   40. The computer-implemented method of clause 39, wherein    determining the customer specific liquidity score for the financial    instrument is based on a machine learning model.

-   41. The computer-implemented method of clause 40, wherein the    machine learning model determines a customer value that is    indicative of a probability that a specific customer will execute a    trade for the security at the trade parameters, wherein the customer    specific liquidity score for the financial instrument is based on    the customer value.

-   42. The computer-implemented method of clause 41, wherein the    machine learning model determines the customer specific liquidity    score based on LSi=(LSmarket+10*(CACi/2), 10), where LSi is the    customer specific liquidity score for customer i; LSmarket is a    liquidity score for the financial instrument irrespective of    customer; and CACi is the customer value.

-   43. The computer-implemented method of clause 21, wherein the    identifying the one or more dealer users capable of acting as the    intermediate entity for trading the financial instrument comprises:    -   determining a cloud score for the financial instrument based on        trading intentions of the one or more invitee users and the        various terms, wherein the cloud score is representative of a        likelihood that the financial instrument will be traded at the        various terms;    -   identifying at least one high likelihood invitee user of the one        or more invitee users based on the cloud score, wherein the at        least one high likelihood invitee user has expressed trading        intentions compatible with successfully trading the financial        instrument with the initiating user at the various terms; and    -   identifying the one or more dealer users having a previous        trading relationship with the at least one high likelihood        invitee user.

-   44. The computer-implemented method of clause 43, wherein    determining the cloud score for the financial instrument is based on    a machine learning model.

-   45. The computer-implemented method of clause 44, wherein the    machine learning model determines the cloud score based on a degree    of matching between the trading intentions of the one or more    invitee users and the various terms.

-   46. The computer-implemented method of clause 45, wherein the degree    of matching is based on a price matching score and a quantity    matching score.

-   47. The computer-implemented method of clause 46, wherein the price    matching score corresponds to a price degree of matching between a    trading price of the trading intentions of the one or more invitee    users and a desired price of the various terms.

-   48. The computer-implemented method of clause 47, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   49. The computer-implemented method of clause 46, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   50. The computer-implemented method of clause 45, wherein the degree    of matching is expressed as a number between 0 and 1.

-   51. The computer-implemented method of clause 21, further    comprising:    -   determining, at the computing device, a cloud score for the        financial instrument based on trading intentions of a group of        potential invitee users and the various terms, wherein the cloud        score is representative of a likelihood that the financial        instrument will be traded at the various terms;    -   ranking, at the computing device, the group of potential invitee        users into a ranked list based on the cloud score to identify        one or more high likelihood potential invitee users that have        expressed trading intentions compatible with successfully        trading the financial instrument with the initiating user at the        various terms; and    -   providing, from the computing device and to the particular        dealer user, the ranked list, wherein the particular dealer user        selects the list of the one or more invitee users to participate        in the electronic communication session from the ranked list.

-   52. The computer-implemented method of clause 51, wherein the ranked    list is limited to a predetermined number of potential invitee    users.

-   53. The computer-implemented method of clause 51, wherein    determining the cloud score for the financial instrument is based on    a machine learning model.

-   54. The computer-implemented method of clause 53, wherein the    machine learning model determines the cloud score based on a degree    of matching between the trading intentions of the one or more    invitee users and the various terms.

-   55. The computer-implemented method of clause 54, wherein the degree    of matching is based on a price matching score and a quantity    matching score.

-   56. The computer-implemented method of clause 55, wherein the price    matching score corresponds to a price degree of matching between a    trading price of the trading intentions of the one or more invitee    users and a desired price of the various terms.

-   57. The computer-implemented method of clause 56, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   58. The computer-implemented method of clause 55, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   59. The computer-implemented method of clause 54, wherein the degree    of matching is expressed as a number between 0 and 1.

-   60. A computing device for hosting an electronic communication    session for trading a financial instrument between an initiating    user and one or more invitee users, the computing device comprising:    -   one or more processors; and    -   a non-transitory computer-readable storage medium having a        plurality of instructions stored thereon, which, when executed        by the one or more processors, cause the one or more processors        to perform operations comprising:        -   receiving a request to begin the electronic communication            session from an initiating user, the request identifying the            financial instrument to be traded and various terms at which            the initiating user is willing to trade the financial            instrument;        -   identifying one or more dealer users capable of acting as an            intermediate entity for trading the financial instrument            based on the request;        -   providing, to the initiating user, a list of the one or more            identified dealer users;        -   receiving, from the initiating user, a selection of a            particular dealer user based on the list of the one or more            identified dealer users;        -   receiving, from the particular dealer user, a list of the            one or more invitee users to participate in the electronic            communication session;        -   transmitting, to each of the one or more invitee users, an            invitation to participate in the electronic communication            session;        -   establishing the electronic communication session, wherein            the electronic communication session permits each of the one            or more invitee users to submit an offer to trade the            financial instrument with the initiating user, each offer            comprising an identification of its associated invitee user            and trade parameters including a price and a quantity of the            financial instrument; and        -   selectively sharing information regarding offers received            during the electronic communication session with each of the            initiating user, the dealer user, and the one or more            invitee users, wherein the initiating user will receive the            trade parameters for each received offer, the dealer user            will receive the identification of the trade parameters for            each received offer and its associated invitee user, and            each particular invitee user will receive either: (i) the            trade parameters for each received offer when the particular            invitee user has submitted its own offer, or (ii) no            information regarding the received offers when the            particular invitee user has not yet submitted its own offer.

-   61. The computing device of clause 60, wherein the identifying the    one or more dealer users capable of acting as the intermediate    entity for trading the financial instrument comprises:    -   determining a liquidity score for the financial instrument based        on historical trading data for one or more other financial        instruments related to the financial instrument;    -   identifying historical dealers associated with the historical        trading data.

-   62. The computing device of clause 61, wherein determining the    liquidity score for the financial instrument is based on a machine    learning model.

-   63. The computing device of clause 62, wherein the machine learning    model determines the liquidity score based on a number of historical    transactions and a volume of historical transactions for the    financial instrument to be traded.

-   64. The computing device of clause 63, wherein the machine learning    model determines the liquidity score based on a count ratio of the    number of historical transactions for the financial instrument to be    traded to a maximum number of historical transactions for any    financial instrument observed.

-   65. The computing device of clause 64, wherein the machine learning    model determines the liquidity score based on a volume ratio of the    volume of historical transactions for the financial instrument to be    traded to a maximum volume of historical transactions for any    financial instrument observed.

-   66. The computing device of clause 65, wherein the machine learning    model determines the liquidity score based on a combination of the    count ratio and the volume ratio.

-   67. The computing device of clause 63, wherein the machine learning    model determines the liquidity score based on a volume ratio of the    volume of historical transactions for the financial instrument to be    traded to a maximum volume of historical transactions for any    financial instrument observed.

-   68. The computing device of clause 60, wherein the identifying the    one or more dealer users capable of acting as the intermediate    entity for trading the financial instrument comprises:    -   determining a dealer specific liquidity score for the financial        instrument based on historical trading data for one or more        other financial instruments related to the financial instrument;    -   identifying historical dealers associated with the historical        trading data.

-   69. The computing device of clause 68, wherein determining the    dealer specific liquidity score for the financial instrument is    based on a machine learning model.

-   70. The computing device of clause 69, wherein the machine learning    model determines the dealer specific liquidity score based on a    number of historical transactions with a particular dealer and a    volume of historical transactions for the financial instrument to be    traded with the particular dealer.

-   71. The computing device of clause 70, wherein the machine learning    model determines the dealer specific liquidity score based on a    dealer specific count ratio of the number of historical transactions    for the financial instrument to be traded with the particular dealer    to a maximum number of historical transactions for any financial    instrument observed with the particular dealer.

-   72. The computing device of clause 71, wherein the machine learning    model determines the dealer specific liquidity score based on a    dealer specific volume ratio of the volume of historical    transactions for the financial instrument to be traded with the    particular dealer to a maximum volume of historical transactions for    any financial instrument observed with the particular dealer.

-   73. The computing device of clause 72, wherein the machine learning    model determines the dealer specific liquidity score based on a    combination of the dealer specific count ratio and the dealer    specific volume ratio.

-   74. The computing device of clause 70, wherein the machine learning    model determines the dealer specific liquidity score based on a    volume ratio of the volume of historical transactions for the    financial instrument to be traded with the particular dealer to a    maximum volume of historical transactions for any financial    instrument observed with the particular dealer.

-   75. The computing device of clause 62, wherein the machine learning    model determines the liquidity score based on a pricing deviation    score corresponding to a difference between a traded pricing and a    desired pricing for the financial instrument to be traded, the    traded pricing comprising a previous price at which the financial    instrument was traded and the desired pricing comprising a desired    price at which the initiating user is willing to trade the financial    instrument.

-   76. The computing device of clause 75, wherein the previous price at    which the financial instrument was traded comprises an end of day    price for the financial instrument.

-   77. The computing device of clause 76, wherein the pricing deviation    score is based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

-   -   where ΔLS(F(P_(i))) is the pricing deviation score for a        financial instrument i; REFP_(i) is the reference pricing of a        recently completed trade for the financial instrument i; STD_(i)        is an average standard deviation of the reference pricing and        trade price for the financial instrument i; and IP_(i) is the        desired price for the financial instrument i.

-   78. The computing device of clause 60, wherein the identifying the    one or more dealer users capable of acting as the intermediate    entity for trading the financial instrument comprises:    -   determining a customer specific liquidity score for the        financial instrument based on historical trading data for one or        more other financial instruments related to the financial        instrument;    -   identifying historical dealers associated with the historical        trading data.

-   79. The computing device of clause 78, wherein determining the    customer specific liquidity score for the financial instrument is    based on a machine learning model.

-   80. The computing device of clause 79, wherein the machine learning    model determines a customer value that is indicative of a    probability that a specific customer will execute a trade for the    security at the trade parameters, wherein the customer specific    liquidity score for the financial instrument is based on the    customer value.

-   81. The computing device of clause 80, wherein the machine learning    model determines the customer specific liquidity score based on    LSi=(LSmarket+10*(CACi/2), 10), where LSi is the customer specific    liquidity score for customer i; LSmarket is a liquidity score for    the financial instrument irrespective of customer; and CACi is the    customer value.

-   82. The computing device of clause 60, wherein the identifying the    one or more dealer users capable of acting as the intermediate    entity for trading the financial instrument comprises:    -   determining a cloud score for the financial instrument based on        trading intentions of the one or more invitee users and the        various terms, wherein the cloud score is representative of a        likelihood that the financial instrument will be traded at the        various terms;    -   identifying at least one high likelihood invitee user of the one        or more invitee users based on the cloud score, wherein the at        least one high likelihood invitee user has expressed trading        intentions compatible with successfully trading the financial        instrument with the initiating user at the various terms; and    -   identifying the one or more dealer users having a previous        trading relationship with the at least one high likelihood        invitee user.

-   83. The computing device of clause 82, wherein determining the cloud    score for the financial instrument is based on a machine learning    model.

-   84. The computing device of clause 83, wherein the machine learning    model determines the cloud score based on a degree of matching    between the trading intentions of the one or more invitee users and    the various terms.

-   85. The computing device of clause 84, wherein the degree of    matching is based on a price matching score and a quantity matching    score.

-   86. The computing device of clause 85, wherein the price matching    score corresponds to a price degree of matching between a trading    price of the trading intentions of the one or more invitee users and    a desired price of the various terms.

-   87. The computing device of clause 86, wherein the quantity matching    score corresponds to a quantity degree of matching between a trading    quantity of the trading intentions of the one or more invitee users    and a desired quantity of the various terms.

-   88. The computing device of clause 85, wherein the quantity matching    score corresponds to a quantity degree of matching between a trading    quantity of the trading intentions of the one or more invitee users    and a desired quantity of the various terms.

-   89. The computing device of clause 84, wherein the degree of    matching is expressed as a number between 0 and 1.

-   90. The computing device of clause 60, wherein the operations    further comprise:    -   determining a cloud score for the financial instrument based on        trading intentions of a group of potential invitee users and the        various terms, wherein the cloud score is representative of a        likelihood that the financial instrument will be traded at the        various terms;    -   ranking the group of potential invitee users into a ranked list        based on the cloud score to identify one or more high likelihood        potential invitee users that have expressed trading intentions        compatible with successfully trading the financial instrument        with the initiating user at the various terms; and    -   providing, to the particular dealer user, the ranked list,    -   wherein the particular dealer user selects the list of the one        or more invitee users to participate in the electronic        communication session from the ranked list.

-   91. The computing device of clause 90, wherein the ranked list is    limited to a predetermined number of potential invitee users.

-   92. The computing device of clause 90, wherein determining the cloud    score for the financial instrument is based on a machine learning    model.

-   93. The computing device of clause 92, wherein the machine learning    model determines the cloud score based on a degree of matching    between the trading intentions of the one or more invitee users and    the various terms.

-   94. The computing device of clause 93, wherein the degree of    matching is based on a price matching score and a quantity matching    score.

-   95. The computing device of clause 94, wherein the price matching    score corresponds to a price degree of matching between a trading    price of the trading intentions of the one or more invitee users and    a desired price of the various terms.

-   96. The computing device of clause 95, wherein the quantity matching    score corresponds to a quantity degree of matching between a trading    quantity of the trading intentions of the one or more invitee users    and a desired quantity of the various terms.

-   97. The computing device of clause 94, wherein the quantity matching    score corresponds to a quantity degree of matching between a trading    quantity of the trading intentions of the one or more invitee users    and a desired quantity of the various terms.

-   98. The computing device of clause 93, wherein the degree of    matching is expressed as a number between 0 and 1.

-   99. A computer-implemented method of determining a likelihood of    successfully trading a particular financial instrument between an    initiating user and one or more invitee users and at various terms    during an electronic communication session, comprising:    -   receiving, at a computing device having one or more processors,        historical trading data associated with previously executed        trade transactions for a plurality of financial instruments,        wherein each of the plurality of financial instruments has        associated characteristics, the associated characteristics        including at least an expected return, and the trade        transactions identify at least a price and quantity traded;    -   storing, at the computing device, the historical trading data;    -   identifying, at the computing device, one or more of the        plurality of financial instruments having associated        characteristics similar to the particular financial instrument;        and    -   determining, at the computing device and using a first machine        learning model, a liquidity score for the particular financial        instrument based on the historical trading data for the one or        more of the plurality of financial instruments having associated        characteristics similar to the particular financial instrument,        the liquidity score being representative of a likelihood of        successfully trading the particular financial instrument at the        various terms.

-   100. The computer-implemented method of clause 99, wherein the first    machine learning model determines the liquidity score based on a    number of historical transactions and a volume of historical    transactions for the financial instrument to be traded.

-   101. The computer-implemented method of clause 100, wherein the    first machine learning model determines the liquidity score based on    a count ratio of the number of historical transactions for the    financial instrument to be traded to a maximum number of historical    transactions for any financial instrument observed.

-   102. The computer-implemented method of clause 101, wherein the    machine learning model determines the liquidity score based on a    volume ratio of the volume of historical transactions for the    financial instrument to be traded to a maximum volume of historical    transactions for any financial instrument observed.

-   103. The computer-implemented method of clause 102, wherein the    machine learning model determines the liquidity score based on a    combination of the count ratio and the volume ratio.

-   104. The computer-implemented method of clause 100, wherein the    machine learning model determines the liquidity score based on a    volume ratio of the volume of historical transactions for the    financial instrument to be traded to a maximum volume of historical    transactions for any financial instrument observed.

-   105. The computer-implemented method of clause 99, wherein the    liquidity score is based on a dealer specific liquidity score.

-   106. The computer-implemented method of clause 105, wherein the    first machine learning model determines the dealer specific    liquidity score based on a number of historical transactions with a    particular dealer and a volume of historical transactions for the    financial instrument to be traded with the particular dealer.

-   107. The computer-implemented method of clause 106, wherein the    first machine learning model determines the dealer specific    liquidity score based on a dealer specific count ratio of the number    of historical transactions for the financial instrument to be traded    with the particular dealer to a maximum number of historical    transactions for any financial instrument observed with the    particular dealer.

-   108. The computer-implemented method of clause 107, wherein the    first machine learning model determines the dealer specific    liquidity score based on a dealer specific volume ratio of the    volume of historical transactions for the financial instrument to be    traded with the particular dealer to a maximum volume of historical    transactions for any financial instrument observed with the    particular dealer.

-   109. The computer-implemented method of clause 108, wherein the    first machine learning model determines the dealer specific    liquidity score based on a combination of the dealer specific count    ratio and the dealer specific volume ratio.

-   110. The computer-implemented method of clause 106, wherein the    first machine learning model determines the dealer specific    liquidity score based on a volume ratio of the volume of historical    transactions for the financial instrument to be traded with the    particular dealer to a maximum volume of historical transactions for    any financial instrument observed with the particular dealer.

-   111. The computer-implemented method of clause 99, wherein the first    machine learning model determines the liquidity score based on a    pricing deviation score corresponding to a difference between a    traded pricing and a desired pricing for the financial instrument to    be traded, the traded pricing comprising a previous price at which    the financial instrument was traded and the desired pricing    comprising a desired price at which the initiating user is willing    to trade the financial instrument.

-   112. The computer-implemented method of clause 111, wherein the    previous price at which the financial instrument was traded    comprises an end of day price for the financial instrument.

-   113. The computer-implemented method of clause 112, wherein the    pricing deviation score is based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

where ΔLS(F(P_(i))) is the pricing deviation score for a financialinstrument i; REFP_(i) is the reference pricing of a recently completedtrade for the financial instrument i; STD_(i) is an average standarddeviation of the reference pricing and trade price for the financialinstrument i; and IP_(i) is the desired price for the financialinstrument i.

-   114. The computer-implemented method of clause 99, wherein the    liquidity score is based on a customer specific liquidity score.-   115. The computer-implemented method of clause 114, wherein the    first machine learning model determines a customer value that is    indicative of a probability that a specific customer will execute a    trade for the security at the trade parameters, wherein the customer    specific liquidity score for the financial instrument is based on    the customer value.-   116. The computer-implemented method of clause 115, wherein the    machine learning model determines the customer specific liquidity    score based on LSi=(LSmarket+10*(CACi/2), 10), where LSi is the    customer specific liquidity score for customer i; LSmarket is a    liquidity score for the financial instrument irrespective of    customer; and CACi is the customer value.-   117. The computer-implemented method of clause 99, further    comprising:    -   receiving, at the computing device, trading intentions from the        one or more invitee users, each trading intention representing a        price and a quantity at which its associated invitee user would        trade an associated financial instrument, each associated        financial instrument having associated characteristics, the        associated characteristics including at least an expected        return;    -   storing, at the computing device, the trading intentions;    -   identifying, at the computing device, one or more associated        financial instruments similar to the particular financial        instrument; and    -   determining, at the computing device and using a second machine        learning model, a cloud score for the particular financial        instrument based on the trading intentions for the one or more        associated financial instrument similar to the particular        financial instrument, the cloud score being representative of a        likelihood of successfully trading the particular financial        instrument at the various terms.-   118. The computer-implemented method of clause 117, wherein the    second machine learning model determines the cloud score based on a    degree of matching between the trading intentions of the one or more    invitee users and the various terms.-   119. The computer-implemented method of clause 118, wherein the    degree of matching is based on a price matching score and a quantity    matching score.-   120. The computer-implemented method of clause 119, wherein the    price matching score corresponds to a price degree of matching    between a trading price of the trading intentions of the one or more    invitee users and a desired price of the various terms.-   121. The computer-implemented method of clause 120, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.-   122. The computer-implemented method of clause 119, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.-   123. The computer-implemented method of clause 118, wherein the    degree of matching is expressed as a number between 0 and 1.-   124. The computer-implemented method of clause 117, further    comprising outputting, from the computing device and to an    initiating user computing device associated with the initiating    user, the liquidity score and the cloud score in a graphical user    interface.-   125. The computer-implemented method of clause 124, wherein    outputting the liquidity score and the cloud score in the graphical    user interface comprises outputting a single score representing a    combination of the liquidity score and the cloud score.-   126. A computing device for determining a likelihood of successfully    trading a particular financial instrument between an initiating user    and one or more invitee users and at various terms during an    electronic communication session, comprising:    -   one or more processors; and    -   a non-transitory computer-readable storage medium having a        plurality of instructions stored thereon, which, when executed        by the one or more processors, cause the one or more processors        to perform operations comprising:        -   receiving historical trading data associated with previously            executed trade transactions for a plurality of financial            instruments, wherein each of the plurality of financial            instruments has associated characteristics, the associated            characteristics including at least an expected return, and            the trade transactions identify at least a price and            quantity traded;        -   storing the historical trading data;        -   identifying one or more of the plurality of financial            instruments having associated characteristics similar to the            particular financial instrument; and        -   determining, using a first machine learning model, a            liquidity score for the particular financial instrument            based on the historical trading data for the one or more of            the plurality of financial instruments having associated            characteristics similar to the particular financial            instrument, the liquidity score being representative of a            likelihood of successfully trading the particular financial            instrument at the various terms.-   127. The computing device of clause 126, wherein the first machine    learning model determines the liquidity score based on a number of    historical transactions and a volume of historical transactions for    the financial instrument to be traded.-   128. The computing device of clause 127, wherein the first machine    learning model determines the liquidity score based on a count ratio    of the number of historical transactions for the financial    instrument to be traded to a maximum number of historical    transactions for any financial instrument observed.-   129. The computing device of clause 128, wherein the machine    learning model determines the liquidity score based on a volume    ratio of the volume of historical transactions for the financial    instrument to be traded to a maximum volume of historical    transactions for any financial instrument observed.-   130. The computing device of clause 129, wherein the machine    learning model determines the liquidity score based on a combination    of the count ratio and the volume ratio.-   131. The computing device of clause 127, wherein the machine    learning model determines the liquidity score based on a volume    ratio of the volume of historical transactions for the financial    instrument to be traded to a maximum volume of historical    transactions for any financial instrument observed.-   132. The computing device of clause 125, wherein the liquidity score    is based on a dealer specific liquidity score.-   133. The computing device of clause 132, wherein the first machine    learning model determines the dealer specific liquidity score based    on a number of historical transactions with a particular dealer and    a volume of historical transactions for the financial instrument to    be traded with the particular dealer.-   134. The computing device of clause 133, wherein the first machine    learning model determines the dealer specific liquidity score based    on a dealer specific count ratio of the number of historical    transactions for the financial instrument to be traded with the    particular dealer to a maximum number of historical transactions for    any financial instrument observed with the particular dealer.-   135. The computing device of clause 134, wherein the first machine    learning model determines the dealer specific liquidity score based    on a dealer specific volume ratio of the volume of historical    transactions for the financial instrument to be traded with the    particular dealer to a maximum volume of historical transactions for    any financial instrument observed with the particular dealer.-   136. The computing device of clause 135, wherein the first machine    learning model determines the dealer specific liquidity score based    on a combination of the dealer specific count ratio and the dealer    specific volume ratio.-   137. The computing device of clause 133, wherein the first machine    learning model determines the dealer specific liquidity score based    on a volume ratio of the volume of historical transactions for the    financial instrument to be traded with the particular dealer to a    maximum volume of historical transactions for any financial    instrument observed with the particular dealer.-   138. The computing device of clause 126, wherein the first machine    learning model determines the liquidity score based on a pricing    deviation score corresponding to a difference between a traded    pricing and a desired pricing for the financial instrument to be    traded, the traded pricing comprising a previous price at which the    financial instrument was traded and the desired pricing comprising a    desired price at which the initiating user is willing to trade the    financial instrument.-   139. The computing device of clause 138, wherein the previous price    at which the financial instrument was traded comprises an end of day    price for the financial instrument.-   140. The computing device of clause 139, wherein the pricing    deviation score is based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

-   -   where ΔLS(F(P_(i))) is the pricing deviation score for a        financial instrument i; REFP_(i) is the reference pricing of a        recently completed trade for the financial instrument i; STD_(i)        is an average standard deviation of the reference pricing and        trade price for the financial instrument i; and IP_(i) is the        desired price for the financial instrument i.

-   141. The computing device of clause 126, wherein the liquidity score    is based on a customer specific liquidity score.

-   142. The computing device of clause 141, wherein the first machine    learning model determines a customer value that is indicative of a    probability that a specific customer will execute a trade for the    security at the trade parameters, wherein the customer specific    liquidity score for the financial instrument is based on the    customer value.

-   143. The computing device of clause 142, wherein the machine    learning model determines the customer specific liquidity score    based on LSi=(LSmarket+10*(CACi/2), 10), where LSi is the customer    specific liquidity score for customer i; LSmarket is a liquidity    score for the financial instrument irrespective of customer; and    CACi is the customer value.

-   144. The computing device of clause 126, further comprising:    -   receiving, at the computing device, trading intentions from the        one or more invitee users, each trading intention representing a        price and a quantity at which its associated invitee user would        trade an associated financial instrument, each associated        financial instrument having associated characteristics, the        associated characteristics including at least an expected        return;    -   storing, at the computing device, the trading intentions;    -   identifying, at the computing device, one or more associated        financial instruments similar to the particular financial        instrument; and    -   determining, at the computing device and using a second machine        learning model, a cloud score for the particular financial        instrument based on the trading intentions for the one or more        associated financial instrument similar to the particular        financial instrument, the cloud score being representative of a        likelihood of successfully trading the particular financial        instrument at the various terms.

-   145. The computing device of clause 144, wherein the second machine    learning model determines the cloud score based on a degree of    matching between the trading intentions of the one or more invitee    users and the various terms.

-   146. The computing device of clause 145, wherein the degree of    matching is based on a price matching score and a quantity matching    score.

-   147. The computing device of clause 146, wherein the price matching    score corresponds to a price degree of matching between a trading    price of the trading intentions of the one or more invitee users and    a desired price of the various terms.

-   148. The computing device of clause 147, wherein the quantity    matching score corresponds to a quantity degree of matching between    a trading quantity of the trading intentions of the one or more    invitee users and a desired quantity of the various terms.

-   149. The computing device of clause 146, wherein the quantity    matching score corresponds to a quantity degree of matching between    a trading quantity of the trading intentions of the one or more    invitee users and a desired quantity of the various terms.

-   150. The computing device of clause 145, wherein the degree of    matching is expressed as a number between 0 and 1.

-   151. The computing device of clause 144, further comprising    outputting, from the computing device and to an initiating user    computing device associated with the initiating user, the liquidity    score and the cloud score in a graphical user interface.

-   152. The computer-implemented method of clause 151, wherein    outputting the liquidity score and the cloud score in the graphical    user interface comprises outputting a single score representing a    combination of the liquidity score and the cloud score.

-   153. A computer-implemented method of determining a likelihood of    successfully trading a particular financial instrument between an    initiating user and one or more invitee users and at various terms    during an electronic communication session, comprising    -   receiving, at the computing device, trading intentions from the        one or more invitee users, each trading intention representing a        price and a quantity at which its associated invitee user would        trade an associated financial instrument, each associated        financial instrument having associated characteristics, the        associated characteristics including at least an expected        return;    -   storing, at the computing device, the trading intentions;    -   identifying, at the computing device, one or more associated        financial instruments similar to the particular financial        instrument; and    -   determining, at the computing device and using a machine        learning model, a cloud score for the particular financial        instrument based on the trading intentions for the one or more        associated financial instrument similar to the particular        financial instrument, the cloud score being representative of a        likelihood of successfully trading the particular financial        instrument at the various terms.

-   154. The computer-implemented method of clause 153, wherein the    machine learning model determines the cloud score based on a degree    of matching between the trading intentions of the one or more    invitee users and the various terms.

-   155. The computer-implemented method of clause 154, wherein the    degree of matching is based on a price matching score and a quantity    matching score.

-   156. The computer-implemented method of clause 155, wherein the    price matching score corresponds to a price degree of matching    between a trading price of the trading intentions of the one or more    invitee users and a desired price of the various terms.

-   157. The computer-implemented method of clause 156, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   158. The computer-implemented method of clause 155, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   159. The computer-implemented method of clause 154, wherein the    degree of matching is expressed as a number between 0 and 1.

-   160. The computer-implemented method of clause 153, further    comprising outputting, from the computing device and to an    initiating user computing device associated with the initiating    user, the cloud score in a graphical user interface.

-   161. A computing device for determining a likelihood of successfully    trading a particular financial instrument between an initiating user    and one or more invitee users and at various terms during an    electronic communication session, comprising:    -   one or more processors; and    -   a non-transitory computer-readable storage medium having a        plurality of instructions stored thereon, which, when executed        by the one or more processors, cause the one or more processors        to perform operations comprising:        -   receiving trading intentions from the one or more invitee            users, each trading intention representing a price and a            quantity at which its associated invitee user would trade an            associated financial instrument, each associated financial            instrument having associated characteristics, the associated            characteristics including at least an expected return;        -   storing the trading intentions;        -   identifying one or more associated financial instruments            similar to the particular financial instrument; and        -   determining, using a machine learning model, a cloud score            for the particular financial instrument based on the trading            intentions for the one or more associated financial            instrument similar to the particular financial instrument,            the cloud score being representative of a likelihood of            successfully trading the particular financial instrument at            the various terms.

-   162. The computing device of clause 161, wherein the machine    learning model determines the cloud score based on a degree of    matching between the trading intentions of the one or more invitee    users and the various terms.

-   163. The computing device of clause 162, wherein the degree of    matching is based on a price matching score and a quantity matching    score.

-   164. The computing device of clause 163, wherein the price matching    score corresponds to a price degree of matching between a trading    price of the trading intentions of the one or more invitee users and    a desired price of the various terms.

-   165. The computing device of clause 164, wherein the quantity    matching score corresponds to a quantity degree of matching between    a trading quantity of the trading intentions of the one or more    invitee users and a desired quantity of the various terms.

-   166. The computing device of clause 163, wherein the quantity    matching score corresponds to a quantity degree of matching between    a trading quantity of the trading intentions of the one or more    invitee users and a desired quantity of the various terms.

-   167. The computing device of clause 162, wherein the degree of    matching is expressed as a number between 0 and 1.

-   168. The computing device of clause 161, further comprising    outputting, from the computing device and to an initiating user    computing device associated with the initiating user, the cloud    score in a graphical user interface.

-   169. A computer-implemented method, comprising:    -   hosting, by a computing device having one or more processors, an        electronic communication session for trading a financial        instrument between an initiating user and one or more invitee        users, wherein the hosting comprises:        -   receiving, by the computing device, a request to begin the            electronic communication session from an initiating user            computing device associated with an initiating user, the            request identifying the financial instrument to be traded            and various terms at which the initiating user is willing to            trade the financial instrument,        -   matching, by the computing device, the request with            capabilities of a plurality of dealer users to identify one            or more dealer users capable of acting as an intermediate            entity for trading the financial instrument,        -   providing, by the computing device and to the initiating            user computing device associated with the initiating user, a            list of the one or more identified dealer users,        -   receiving, by the computing device and from the initiating            user computing device associated with the initiating user, a            selection of a particular dealer user based on the list of            the one or more identified dealer users,    -   in response to receiving the selection of the particular dealer        user, establishing, by the computing device, the electronic        communication session, wherein the establishing comprises:        -   receiving, by the computing device and from a dealer user            computing device associated with the particular dealer user,            a list of the one or more invitee users to participate in            the electronic communication session; and        -   in response to receiving the list of the one or more invitee            users, transmitting, by the computing device and to each            invitee user computing device associated with each of the            one or more invitee users, an invitation to participate in            the electronic communication session;    -   coordinating, by the computing device, communications during the        electronic communication session, wherein the electronic        communication session permits each of the one or more invitee        users to submit an offer to trade the financial instrument with        the initiating user, each offer comprising an identification of        its associated invitee user and trade parameters including a        price and a quantity of the financial instrument, and    -   wherein coordinating communications during the electronic        communication session comprises selectively sharing, by the        computing device, information regarding offers received during        the electronic communication session with each of the initiating        user, the dealer user, and the one or more invitee users,        wherein the initiating user receives the trade parameters for        each received offer, the dealer user receives the identification        of the trade parameters for each received offer and its        associated invitee user, and each particular invitee user        receives: (i) the trade parameters for each received offer when        the particular invitee user has submitted its own offer,        and (ii) no information regarding the received offers when the        particular invitee user has not yet submitted its own offer.

-   170. The computer-implemented method of claim 169, wherein the    identifying the one or more dealer users capable of acting as the    intermediate entity for trading the financial instrument comprises:    -   determining a liquidity score for the financial instrument based        on historical trading data for one or more other financial        instruments related to the financial instrument;    -   identifying historical dealers associated with the historical        trading data.

-   171. The computer-implemented method of claim 170, wherein    determining the liquidity score for the financial instrument is    based on a liquidity score machine learning model.

-   172. The computer-implemented method of claim 171, wherein the    liquidity score machine learning model determines the liquidity    score based on a number of historical transactions and a volume of    historical transactions for the financial instrument to be traded.

-   173. The computer-implemented method of claim 172, wherein the    liquidity score machine learning model determines the liquidity    score based on a count ratio of the number of historical    transactions for the financial instrument to be traded to a maximum    number of historical transactions for any financial instrument    observed.

-   174. The computer-implemented method of claim 173, wherein the    liquidity score machine learning model determines the liquidity    score based on a volume ratio of the volume of historical    transactions for the financial instrument to be traded to a maximum    volume of historical transactions for any financial instrument    observed.

-   175. The computer-implemented method of claim 174, wherein the    liquidity score machine learning model determines the liquidity    score based on a combination of the count ratio and the volume    ratio.

-   176. The computer-implemented method of claim 172, wherein the    liquidity score machine learning model determines the liquidity    score based on a volume ratio of the volume of historical    transactions for the financial instrument to be traded to a maximum    volume of historical transactions for any financial instrument    observed.

-   177. The computer-implemented method of claim 169, wherein the    identifying the one or more dealer users capable of acting as the    intermediate entity for trading the financial instrument comprises:    -   determining a dealer specific liquidity score for the financial        instrument based on historical trading data for one or more        other financial instruments related to the financial instrument;    -   identifying historical dealers associated with the historical        trading data.

-   178. The computer-implemented method of claim 177, wherein    determining the dealer specific liquidity score for the financial    instrument is based on a liquidity score machine learning model.

-   179. The computer-implemented method of claim 178, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a number of historical    transactions with a particular dealer and a volume of historical    transactions for the financial instrument to be traded with the    particular dealer.

-   180. The computer-implemented method of claim 179, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a dealer specific count ratio of    the number of historical transactions for the financial instrument    to be traded with the particular dealer to a maximum number of    historical transactions for any financial instrument observed with    the particular dealer.

-   181. The computer-implemented method of claim 180, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a dealer specific volume ratio of    the volume of historical transactions for the financial instrument    to be traded with the particular dealer to a maximum volume of    historical transactions for any financial instrument observed with    the particular dealer.

-   182. The computer-implemented method of claim 181, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a combination of the dealer    specific count ratio and the dealer specific volume ratio.

-   183. The computer-implemented method of claim 179, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a volume ratio of the volume of    historical transactions for the financial instrument to be traded    with the particular dealer to a maximum volume of historical    transactions for any financial instrument observed with the    particular dealer.

-   184. The computer-implemented method of claim 181, wherein the    liquidity score machine learning model determines the liquidity    score based on a pricing deviation score corresponding to a    difference between a traded pricing and a desired pricing for the    financial instrument to be traded, the traded pricing comprising a    previous price at which the financial instrument was traded and the    desired pricing comprising a desired price at which the initiating    user is willing to trade the financial instrument.

-   185. The computer-implemented method of claim 184, wherein the    previous price at which the financial instrument was traded    comprises an end of day price for the financial instrument.

-   186. The computer-implemented method of claim 185, wherein the    pricing deviation score is based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

-   -   where ΔLS(F(P_(i))) is the pricing deviation score for a        financial instrument i; REFP_(i) is the reference pricing of a        recently completed trade for the financial instrument i; STD_(i)        is an average standard deviation of the reference pricing and        trade price for the financial instrument i; and IP_(i) is the        desired price for the financial instrument i.

-   187. The computer-implemented method of claim 169, wherein the    identifying the one or more dealer users capable of acting as the    intermediate entity for trading the financial instrument comprises:    -   determining a customer specific liquidity score for the        financial instrument based on historical trading data for one or        more other financial instruments related to the financial        instrument;    -   identifying historical dealers associated with the historical        trading data.

-   188. The computer-implemented method of claim 187, wherein    determining the customer specific liquidity score for the financial    instrument is based on a liquidity score machine learning model.

-   189. The computer-implemented method of claim 188, wherein the    liquidity score machine learning model determines a customer value    that is indicative of a probability that a specific customer will    execute a trade for the security at the trade parameters, wherein    the customer specific liquidity score for the financial instrument    is based on the customer value.

-   190. The computer-implemented method of claim 189, wherein the    liquidity score machine learning model determines the customer    specific liquidity score based on LSi=(LSmarket+10*(CACi/2), 10),    where LSi is the customer specific liquidity score for customer i;    LSmarket is a liquidity score for the financial instrument    irrespective of customer; and CACi is the customer value.

-   191. The computer-implemented method of claim 169, wherein the    identifying the one or more dealer users capable of acting as the    intermediate entity for trading the financial instrument comprises:    -   determining a cloud score for the financial instrument based on        trading intentions of the one or more invitee users and the        various terms, wherein the cloud score is representative of a        likelihood that the financial instrument will be traded at the        various terms;    -   identifying at least one high likelihood invitee user of the one        or more invitee users based on the cloud score, wherein the at        least one high likelihood invitee user has expressed trading        intentions compatible with successfully trading the financial        instrument with the initiating user at the various terms; and    -   identifying the one or more dealer users having a previous        trading relationship with the at least one high likelihood        invitee user.

-   192. The computer-implemented method of claim 191, wherein    determining the cloud score for the financial instrument is based on    a cloud score machine learning model.

-   193. The computer-implemented method of claim 192, wherein the cloud    score machine learning model determines the cloud score based on a    degree of matching between the trading intentions of the one or more    invitee users and the various terms.

-   194. The computer-implemented method of claim 193, wherein the    degree of matching is based on a price matching score and a quantity    matching score.

-   195. The computer-implemented method of claim 194, wherein the price    matching score corresponds to a price degree of matching between a    trading price of the trading intentions of the one or more invitee    users and a desired price of the various terms.

-   196. The computer-implemented method of claim 195, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   197. The computer-implemented method of claim 194, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   198. The computer-implemented method of claim 193, wherein the    degree of matching is expressed as a number between 0 and 1.

-   199. The computer-implemented method of claim 169, further    comprising:    -   determining, by the computing device, a cloud score for the        financial instrument based on trading intentions of a group of        potential invitee users and the various terms, wherein the cloud        score is representative of a likelihood that the financial        instrument will be traded at the various terms;    -   ranking, by the computing device, the group of potential invitee        users into a ranked list based on the cloud score to identify        one or more high likelihood potential invitee users that have        expressed trading intentions compatible with successfully        trading the financial instrument with the initiating user at the        various terms; and    -   providing, by the computing device and to a particular dealer        user computing device associated with the particular dealer        user, the ranked list,    -   wherein the particular dealer user selects the list of the one        or more invitee users to participate in the electronic        communication session from the ranked list.

-   200. The computer-implemented method of claim 199, wherein the    ranked list is limited to a predetermined number of potential    invitee users.

-   201. The computer-implemented method of claim 199, wherein    determining the cloud score for the financial instrument is based on    a cloud score machine learning model.

-   202. The computer-implemented method of claim 201, wherein the cloud    score machine learning model determines the cloud score based on a    degree of matching between the trading intentions of the one or more    invitee users and the various terms.

-   203. The computer-implemented method of claim 202, wherein the    degree of matching is based on a price matching score and a quantity    matching score.

-   204. The computer-implemented method of claim 203, wherein the price    matching score corresponds to a price degree of matching between a    trading price of the trading intentions of the one or more invitee    users and a desired price of the various terms.

-   205. The computer-implemented method of claim 204, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   206. The computer-implemented method of claim 203, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.

-   207. The computer-implemented method of claim 202, wherein the    degree of matching is expressed as a number between 0 and 1.

-   208. A computer-implemented method, comprising:    -   determining, by a computing device having one or more        processors, a likelihood of executing a trade of a particular        financial instrument between an initiating user and one or more        invitee users and at various terms during an electronic        communication session, wherein the determining comprises:        -   receiving, by the computing device, historical trading data            associated with previously executed trade transactions for a            plurality of financial instruments, wherein each of the            plurality of financial instruments has associated instrument            characteristics, the associated instrument characteristics            including at least an expected return, and the trade            transactions identify at least a price and quantity traded,        -   performing, by the computing device, cluster analysis of the            particular financial instrument and the plurality of            financial instruments to identify one or more of the            plurality of financial instruments having associated            instrument characteristics that match instrument            characteristics of the particular financial instrument, and        -   determining, by the computing device and using a liquidity            score machine learning model, a liquidity score for the            particular financial instrument based on the historical            trading data for the one or more of the plurality of            financial instruments having associated instrument            characteristics that match the instrument characteristics of            the particular financial instrument, the liquidity score            being representative of a likelihood of executing a trade of            the particular financial instrument at the various terms;            and    -   ranking, by the computing device, the particular financial        instrument and other financial instruments into a ranked list        based on the liquidity score and other liquidity scores        associated with the other financial instruments; and    -   presenting, by the computing device, the ranked list in a        graphical user interface of an initiating user computing device        associated with the initiating user.

-   209. The computer-implemented method of claim 208, wherein the    liquidity score machine learning model determines the liquidity    score based on a number of historical transactions and a volume of    historical transactions for the particular financial instrument to    be traded.

-   210. The computer-implemented method of claim 209, wherein the    liquidity score machine learning model determines the liquidity    score based on a count ratio of the number of historical    transactions for the particular financial instrument to be traded to    a maximum number of historical transactions for any financial    instrument observed.

-   211. The computer-implemented method of claim 210, wherein the    liquidity score machine learning model determines the liquidity    score based on a volume ratio of the volume of historical    transactions for the particular financial instrument to be traded to    a maximum volume of historical transactions for any financial    instrument observed.

-   212. The computer-implemented method of claim 211, wherein the    liquidity score machine learning model determines the liquidity    score based on a combination of the count ratio and the volume    ratio.

-   213. The computer-implemented method of claim 209, wherein the    liquidity score machine learning model determines the liquidity    score based on a volume ratio of the volume of historical    transactions for the particular financial instrument to be traded to    a maximum volume of historical transactions for any financial    instrument observed.

-   214. The computer-implemented method of claim 208, wherein the    liquidity score is based on a dealer specific liquidity score.

-   215. The computer-implemented method of claim 214, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a number of historical    transactions with a particular dealer and a volume of historical    transactions for the particular financial instrument to be traded    with the particular dealer.

-   216. The computer-implemented method of claim 215, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a dealer specific count ratio of    the number of historical transactions for the particular financial    instrument to be traded with the particular dealer to a maximum    number of historical transactions for any financial instrument    observed with the particular dealer.

-   217. The computer-implemented method of claim 216, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a dealer specific volume ratio of    the volume of historical transactions for the particular financial    instrument to be traded with the particular dealer to a maximum    volume of historical transactions for any financial instrument    observed with the particular dealer.

-   218. The computer-implemented method of claim 217, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a combination of the dealer    specific count ratio and the dealer specific volume ratio.

-   219. The computer-implemented method of claim 215, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a volume ratio of the volume of    historical transactions for the particular financial instrument to    be traded with the particular dealer to a maximum volume of    historical transactions for any financial instrument observed with    the particular dealer.

-   220. The computer-implemented method of claim 208, wherein the    liquidity score machine learning model determines the liquidity    score based on a pricing deviation score corresponding to a    difference between a traded pricing and a desired pricing for the    particular financial instrument to be traded, the traded pricing    comprising a previous price at which the particular financial    instrument was traded and the desired pricing comprising a desired    price at which the initiating user is willing to trade the    particular financial instrument.

-   221. The computer-implemented method of claim 220, wherein the    previous price at which the particular financial instrument was    traded comprises an end of day price for the particular financial    instrument.

-   222. The computer-implemented method of claim 220, wherein the    pricing deviation score is based on the equation:

${\Delta\;{{LS}\left( {F\left( P_{i} \right)} \right)}} = \frac{{REFP_{i}} - {IP}_{i}}{\overset{\_}{{ST}D_{i}}*0.5}$

where ΔLS(F(P_(i))) is the pricing deviation score for a financialinstrument i; REFP_(i) is the reference pricing of a recently completedtrade for the financial instrument i; STD_(i) is an average standarddeviation of the reference pricing and trade price for the financialinstrument i; and IP_(i) is the desired price for the financialinstrument i.

-   223. The computer-implemented method of claim 208, wherein the    liquidity score is based on a customer specific liquidity score.-   224. The computer-implemented method of claim 223, wherein the    liquidity score machine learning model determines a customer value    that is indicative of a probability that a specific customer will    execute the trade for the particular financial instrument at the    trade parameters, wherein the customer specific liquidity score for    the particular financial instrument is based on the customer value.-   225. The computer-implemented method of claim 224, wherein the    liquidity score machine learning model determines the customer    specific liquidity score based on LSi=(LSmarket+10*(CACi/2), 10),    where LSi is the customer specific liquidity score for customer i;    LSmarket is a liquidity score for the particular financial    instrument irrespective of customer; and CACi is the customer value.-   226. The computer-implemented method of claim 208, further    comprising:    -   receiving, by the computing device, trading intentions from one        or more invitee user computing devices associated with the one        or more invitee users, each trading intention representing a        price and a quantity at which its associated invitee user would        trade an associated financial instrument, each associated        financial instrument having associated instrument        characteristics, the associated instrument characteristics        including at least an expected return;    -   performing, by the computing device, cluster analysis of the        particular financial instrument and the associated financial        instruments to identify one or more associated financial        instruments having associated instrument characteristics that        match instrument characteristics of the particular financial        instrument;    -   determining, by the computing device and using a cloud score        machine learning model, a cloud score for the particular        financial instrument based on the trading intentions for the one        or more associated financial instruments having associated        instrument characteristics that match the instrument        characteristics of the particular financial instrument, the        cloud score being representative of a likelihood of executing        the trade of the particular financial instrument at the various        terms; and    -   outputting, by the computing device and to the initiating user        computing device associated with the initiating user, the        liquidity score and the cloud score in the graphical user        interface.-   227. The computer-implemented method of claim 226, wherein the cloud    score machine learning model determines the cloud score based on a    degree of matching between the trading intentions of the one or more    invitee users and the various terms.-   228. The computer-implemented method of claim 227, wherein the    degree of matching is based on a price matching score and a quantity    matching score.-   229. The computer-implemented method of claim 228, wherein the price    matching score corresponds to a price degree of matching between a    trading price of the trading intentions of the one or more invitee    users and a desired price of the various terms.-   230. The computer-implemented method of claim 229, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.-   231. The computer-implemented method of claim 228, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.-   232. The computer-implemented method of claim 227, wherein the    degree of matching is expressed as a number between 0 and 1.-   233. The computer-implemented method of claim 226, wherein    outputting the liquidity score and the cloud score in the graphical    user interface comprises outputting a single score representing a    combination of the liquidity score and the cloud score.-   234. A computer-implemented method, comprising    -   determining, by a computing device having one or more        processors, a likelihood of executing a trade of a particular        financial instrument between an initiating user and one or more        invitee users and at various terms during an electronic        communication session, wherein the determining comprises:        -   receiving, by the computing device, trading intentions from            one or more invitee user computing devices associated with            the one or more invitee users, each trading intention            representing a price and a quantity at which its associated            invitee user would trade an associated financial instrument,            each associated financial instrument having associated            instrument characteristics, the associated instrument            characteristics including at least an expected return,        -   performing, by the computing device, cluster analysis of the            particular financial instrument and the associated financial            instruments to identify one or more associated financial            instruments having associated instrument characteristics            that match instrument characteristics of the particular            financial instrument, and        -   determining, by the computing device and using a cloud score            machine learning model, a cloud score for the particular            financial instrument based on the trading intentions for the            one or more associated financial instruments having            associated instrument characteristics that match the            instrument characteristics of the particular financial            instrument, the cloud score being representative of a            likelihood of executing the trade of the particular            financial instrument at the various terms;    -   ranking, by the computing device, the particular financial        instrument and other financial instruments into a ranked list        based on the cloud score and other cloud scores associated with        the other financial instruments; and    -   presenting, by the computing device, the ranked list in a        graphical user interface of an initiating user computing device        associated with the initiating user.-   235. The computer-implemented method of claim 234, wherein the cloud    score machine learning model determines the cloud score based on a    degree of matching between the trading intentions of the one or more    invitee users and the various terms.-   236. The computer-implemented method of claim 235, wherein the    degree of matching is based on a price matching score and a quantity    matching score.-   237. The computer-implemented method of claim 236, wherein the price    matching score corresponds to a price degree of matching between a    trading price of the trading intentions of the one or more invitee    users and a desired price of the various terms.-   238. The computer-implemented method of claim 237, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.-   239. The computer-implemented method of claim 236, wherein the    quantity matching score corresponds to a quantity degree of matching    between a trading quantity of the trading intentions of the one or    more invitee users and a desired quantity of the various terms.-   240. The computer-implemented method of claim 235, wherein the    degree of matching is expressed as a number between 0 and 1.-   241. The computer-implemented method of claim 234, further    comprising outputting, from the computing device and to the    initiating user computing device associated with the initiating    user, the cloud score in the graphical user interface.-   242. The computer-implemented method of claim 234, wherein    determining the likelihood of executing the trade of a particular    financial instrument further comprises:    -   receiving, by the computing device, historical trading data        associated with previously executed trade transactions for a        plurality of historically traded financial instruments, wherein        each of the plurality of historically traded financial        instruments has associated historically traded instrument        characteristics, the associated historically traded instrument        characteristics including at least an expected return, and the        trade transactions identify at least a price and quantity        traded;    -   performing, by the computing device, cluster analysis of the        particular financial instrument and the plurality of        historically traded financial instruments to identify one or        more of the plurality of historically traded financial        instruments having associated historically traded instrument        characteristics that match the instrument characteristics of the        particular financial instrument; and    -   determining, by the computing device and using a liquidity score        machine learning model, a liquidity score for the particular        financial instrument based on the historical trading data for        the one or more of the plurality of historically traded        financial instruments having associated historically traded        instrument characteristics that match the instrument        characteristics of the particular financial instrument, the        liquidity score being representative of the likelihood of        executing the trade of the particular financial instrument at        the various terms, wherein the ranked list is further based on        the liquidity score.-   243. The computer-implemented method of claim 242, wherein the    liquidity score machine learning model determines the liquidity    score based on a number of historical transactions and a volume of    historical transactions for the particular financial instrument to    be traded.-   244. The computer-implemented method of claim 243, wherein the    liquidity score machine learning model determines the liquidity    score based on a count ratio of the number of historical    transactions for the particular financial instrument to be traded to    a maximum number of historical transactions for any financial    instrument observed.-   245. The computer-implemented method of claim 244, wherein the    liquidity score machine learning model determines the liquidity    score based on a volume ratio of the volume of historical    transactions for the particular financial instrument to be traded to    a maximum volume of historical transactions for any financial    instrument observed.-   246. The computer-implemented method of claim 245, wherein the    liquidity score machine learning model determines the liquidity    score based on a combination of the count ratio and the volume    ratio.-   247. The computer-implemented method of claim 243, wherein the    liquidity score machine learning model determines the liquidity    score based on a volume ratio of the volume of historical    transactions for the particular financial instrument to be traded to    a maximum volume of historical transactions for any financial    instrument observed.-   248. The computer-implemented method of claim 234, wherein the    liquidity score is based on a dealer specific liquidity score.-   249. The computer-implemented method of claim 248, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a number of historical    transactions with a particular dealer and a volume of historical    transactions for the particular financial instrument to be traded    with the particular dealer.-   250. The computer-implemented method of claim 249, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a dealer specific count ratio of    the number of historical transactions for the particular financial    instrument to be traded with the particular dealer to a maximum    number of historical transactions for any financial instrument    observed with the particular dealer.-   251. The computer-implemented method of claim 250, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a dealer specific volume ratio of    the volume of historical transactions for the particular financial    instrument to be traded with the particular dealer to a maximum    volume of historical transactions for any financial instrument    observed with the particular dealer.-   252. The computer-implemented method of claim 251, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a combination of the dealer    specific count ratio and the dealer specific volume ratio.-   253. The computer-implemented method of claim 249, wherein the    liquidity score machine learning model determines the dealer    specific liquidity score based on a volume ratio of the volume of    historical transactions for the particular financial instrument to    be traded with the particular dealer to a maximum volume of    historical transactions for any financial instrument observed with    the particular dealer.

While a number of embodiments of the present disclosure have beendescribed, it is understood that these embodiments are illustrativeonly, and not restrictive, and that many modifications may becomeapparent to those of ordinary skill in the art, including that theinventive methodologies, the inventive systems, and the inventivedevices described herein can be utilized in any combination with eachother. Further still, the various steps may be carried out in anydesired order (and any desired steps may be added, and/or any desiredsteps may be eliminated).

What is claimed is:
 1. A computing device for hosting an electroniccommunication session for selecting a financial instrument to trade, thecomputing device comprising: one or more processors; and anon-transitory computer-readable storage medium having a plurality ofinstructions stored thereon, which, when executed by the one or moreprocessors, cause the one or more processors to perform operationscomprising: hosting the electronic communication session for theinitiating user to select a target financial instrument to trade,wherein the hosting comprises: receiving a request to begin theelectronic communication session from an initiating user computingdevice associated with the initiating user, the request identifying apreferred risk profile of the initiating user relating to the targetfinancial instrument to be traded; receiving a plurality of datarelating to a plurality of attributes of a plurality of financialinstruments available to trade; identifying one or more identifiedfinancial instruments among the plurality of financial instruments basedin part on a match between the preferred risk profile and at least onedatum among the plurality of data relating to the plurality ofattributes of the plurality of financial instruments available to trade;matching the one or more identified financial instruments to one or moreidentified dealer users capable of acting as an intermediate entity fortrading the one or more identified financial instruments; and providingan alert, to the initiating user computing device associated with theinitiating user, including a list of the one or more identifiedfinancial instruments, wherein the alert is presented in a graphicaluser interface (“GUI”) containing one or more active links to connectwith the one or more identified dealer users, and wherein each of theone or more identified financial instruments is presented in associationwith a measure summarizing in part a degree of match between thepreferred risk profile of a financial instrument and the plurality ofattributes of the one or more identified financial instruments.
 2. Thecomputing device of claim 1, wherein the plurality of attributes includeone or more risk attributes and one or more yield attributes.
 3. Thecomputing device of claim 2, wherein the one or more yield attributesrelates to a coupon type.
 4. The computing device of claim 2, whereinthe one or more yield attributes relates to a coupon rate.
 5. Thecomputing device of claim 4, wherein the coupon rate is a floating rate.6. The computing device of claim 2, wherein the one or more yieldattributes relates to a coupon frequency.
 7. The computing device ofclaim 2, wherein the one or more yield attributes relates to a maturitydate
 8. The computing device of claim 1, wherein the plurality ofattributes of the one or more identified financial instruments includesa spread data similarity measure indicating a presence or absence of adivergence between a market assessment or risk and the credit rating ofa bond.
 9. The computing device of claim 1, wherein the plurality ofattributes of the one or more identified financial instruments includesa presence or absence of a TRACE-eligibility flag.
 10. Acomputer-implemented method of hosting an electronic communicationsession for trading a financial instrument between an initiating userand one or more dealer users, comprising: hosting the electroniccommunication session for the initiating user to select a targetfinancial instrument to trade, wherein the hosting comprises: receivinga request to begin the electronic communication session from aninitiating user computing device associated with the initiating user,the request identifying a preferred risk profile of the initiating userrelating to the target financial instrument to be traded; receiving aplurality of data relating to a plurality of attributes of a pluralityof financial instruments available to trade; identifying one or moreidentified financial instruments among the plurality of financialinstruments based in part on a match between the preferred risk profileand at least one datum among the plurality of data relating to theplurality of attributes of the plurality of financial instrumentsavailable to trade; matching the one or more identified financialinstruments to one or more identified dealer users capable of acting asan intermediate entity for trading the one or more identified financialinstruments; and providing an alert, to the initiating user computingdevice associated with the initiating user, including a list of the oneor more identified financial instruments, wherein the alert is presentedin a graphical user interface (“GUI”) containing one or more activelinks to connect with the one or more identified dealer users, andwherein each of the one or more identified financial instruments ispresented in association with a measure summarizing in part a degree ofmatch between the preferred risk profile of a financial instrument andthe plurality of attributes of the one or more identified financialinstruments.
 11. The computer-implemented method of claim 10, whereinthe plurality of attributes include one or more risk attributes and oneor more yield attributes.
 12. The computer-implemented method of claim11, wherein the one or more yield attributes relates to a coupon type.13. The computer-implemented method of claim 11, wherein the one or moreyield attributes relates to a coupon rate.
 14. The computer-implementedmethod of claim 13, wherein the coupon rate is a floating rate.
 15. Thecomputer-implemented method of claim 11, wherein the one or more yieldattributes relates to a coupon frequency.
 16. The computer-implementedmethod of claim 10, wherein the one or more yield attributes relates toa maturity date
 17. The computer-implemented method of claim 10, whereinthe plurality of attributes of the one or more identified financialinstruments includes a spread data similarity measure indicating apresence or absence of a divergence between a market assessment or riskand the credit rating of a bond.
 18. The computer-implemented method ofclaim 10, wherein the plurality of attributes of the one or moreidentified financial instruments includes a presence or absence of aTRACE-eligibility flag.